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develop/doc/_sources/api/v1/trainer_config_helpers/activations.rst.txt

-===========
-Activations
-===========
-
-BaseActivation
-==============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: BaseActivation
-    :noindex:
-    
-AbsActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: AbsActivation
-    :noindex:
-    
-ExpActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: ExpActivation
-    :noindex:
-    
-IdentityActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: IdentityActivation
-    :noindex:
-    
-LinearActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: LinearActivation
-    :noindex:
-
-LogActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: LogActivation
-    :noindex:
-    
-SquareActivation
-================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SquareActivation
-    :noindex:
-    
-SigmoidActivation
-=================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SigmoidActivation
-    :noindex:
-    
-SoftmaxActivation
-=================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SoftmaxActivation
-    :noindex:
-    
-SequenceSoftmaxActivation
-=========================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SequenceSoftmaxActivation
-    :noindex:
-    
-ReluActivation
-==============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: ReluActivation
-    :noindex:
-    
-BReluActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: BReluActivation
-    :noindex:
-    
-SoftReluActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SoftReluActivation
-    :noindex:
-    
-TanhActivation
-==============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: TanhActivation
-    :noindex:
-    
-STanhActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: STanhActivation
-    :noindex:

develop/doc/_sources/api/v1/trainer_config_helpers/attrs.rst.txt

-Parameter Attributes
-=======================
-
-..  automodule:: paddle.trainer_config_helpers.attrs
-    :members:

develop/doc/_sources/api/v1/trainer_config_helpers/data_sources.rst.txt

-..  _api_trainer_config_helpers_data_sources:
-
-DataSources
-===========
-
-..  automodule:: paddle.trainer_config_helpers.data_sources
-    :members:

develop/doc/_sources/api/v1/trainer_config_helpers/evaluators.rst.txt

-..  _api_trainer_config_helpers_evaluators:
-
-==========
-Evaluators
-==========
-
-Base
-====
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: evaluator_base
-    :noindex:
-
-Classification 
-==============
-
-classification_error_evaluator
-------------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: classification_error_evaluator
-    :noindex:
-
-auc_evaluator
--------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: auc_evaluator
-    :noindex:
-
-ctc_error_evaluator
--------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: ctc_error_evaluator
-    :noindex:
-
-chunk_evaluator
----------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: chunk_evaluator
-    :noindex:
-
-precision_recall_evaluator
---------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  precision_recall_evaluator
-    :noindex:
-
-Rank
-====
-
-pnpair_evaluator
-----------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  pnpair_evaluator
-    :noindex:
-
-Utils
-=====
-
-sum_evaluator
--------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: sum_evaluator
-    :noindex:
-
-column_sum_evaluator
---------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: column_sum_evaluator
-    :noindex:
-
-Print
-=====
-
-classification_error_printer_evaluator
---------------------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  classification_error_printer_evaluator
-    :noindex:
-
-gradient_printer_evaluator
---------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  gradient_printer_evaluator
-    :noindex:
-
-maxid_printer_evaluator
------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  maxid_printer_evaluator
-    :noindex:
-
-maxframe_printer_evaluator
----------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  maxframe_printer_evaluator
-    :noindex:
-
-seqtext_printer_evaluator
--------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  seqtext_printer_evaluator
-    :noindex:
-
-value_printer_evaluator
------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  value_printer_evaluator
-    :noindex:
-

develop/doc/_sources/api/v1/trainer_config_helpers/layers.rst.txt

-..  _api_trainer_config_helpers_layers:
-
-======
-Layers
-======
-
-Base
-======
-
-LayerType
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: LayerType
-    :noindex:
-
-LayerOutput
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: LayerOutput
-    :noindex:
-
-Data layer
-===========
-
-..  _api_trainer_config_helpers_layers_data_layer:
-
-data_layer
-----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: data_layer
-    :noindex:
-
-Fully Connected Layers
-======================
-
-..  _api_trainer_config_helpers_layers_fc_layer:
-
-fc_layer
---------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: fc_layer
-    :noindex:
-
-selective_fc_layer
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: selective_fc_layer
-    :noindex:
-
-Conv Layers
-===========
-
-conv_operator
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: conv_operator
-    :noindex:
-
-conv_projection
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: conv_projection
-    :noindex:
-
-conv_shift_layer
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: conv_shift_layer
-    :noindex:
-
-img_conv_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: img_conv_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_context_projection:
-
-context_projection 
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: context_projection
-    :noindex:
-
-Image Pooling Layer
-===================
-
-img_pool_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: img_pool_layer
-    :noindex:   
-
-spp_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: spp_layer
-    :noindex:
-
-maxout_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: maxout_layer
-    :noindex:
-
-Norm Layer
-==========
-
-img_cmrnorm_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: img_cmrnorm_layer
-    :noindex:
-
-batch_norm_layer
----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: batch_norm_layer
-    :noindex:
-
-sum_to_one_norm_layer
----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sum_to_one_norm_layer
-    :noindex:
-    
-Recurrent Layers
-================
-
-recurrent_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: recurrent_layer
-    :noindex:
-
-lstmemory
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: lstmemory
-    :noindex:
-
-grumemory
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: grumemory
-    :noindex:
-
-Recurrent Layer Group
-=====================
-
-memory
-------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: memory
-    :noindex:
-
-recurrent_group
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: recurrent_group
-    :noindex:
-    
-lstm_step_layer
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: lstm_step_layer
-    :noindex:
-
-gru_step_layer
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: gru_step_layer
-    :noindex:
-
-beam_search
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: beam_search
-    :noindex:
-    
-get_output_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: get_output_layer
-    :noindex:
-    
-Mixed Layer
-===========
-
-..  _api_trainer_config_helpers_layers_mixed_layer:
-
-mixed_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: mixed_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_embedding_layer:
-
-embedding_layer
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: embedding_layer
-    :noindex:
-
-scaling_projection
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: scaling_projection
-    :noindex:
-
-dotmul_projection
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: dotmul_projection
-    :noindex:
-
-dotmul_operator
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: dotmul_operator
-    :noindex:
-
-full_matrix_projection
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: full_matrix_projection
-    :noindex:
-
-identity_projection
--------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: identity_projection
-    :noindex:
-
-
-table_projection
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: table_projection
-    :noindex:
-
-trans_full_matrix_projection
-----------------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: trans_full_matrix_projection
-    :noindex:
-    
-Aggregate Layers
-================
-
-..  _api_trainer_config_helpers_layers_pooling_layer:
-
-pooling_layer
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: pooling_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_last_seq:
-
-last_seq
---------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: last_seq
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_first_seq:
-
-first_seq
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: first_seq
-    :noindex:
-
-concat_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: concat_layer
-    :noindex:
-
-seq_concat_layer
-----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: seq_concat_layer
-    :noindex:
-
-Reshaping Layers
-================
-
-block_expand_layer
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: block_expand_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_expand_layer:
-
-expand_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: expand_layer
-    :noindex:
-
-repeat_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: repeat_layer
-    :noindex:
-
-rotate_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: rotate_layer
-    :noindex:
-
-seq_reshape_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: seq_reshape_layer
-    :noindex:
-
-Math Layers
-===========
-
-addto_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: addto_layer
-    :noindex:
-
-linear_comb_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: linear_comb_layer
-    :noindex:
-
-interpolation_layer
--------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: interpolation_layer
-    :noindex:
-
-bilinear_interp_layer
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: bilinear_interp_layer
-    :noindex:
-
-power_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: power_layer
-    :noindex:
-
-scaling_layer
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: scaling_layer
-    :noindex:
-
-slope_intercept_layer
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: slope_intercept_layer
-    :noindex:
-
-tensor_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: tensor_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_cos_sim:
-
-cos_sim
--------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: cos_sim
-    :noindex:
-
-trans_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: trans_layer
-    :noindex:
-
-Sampling Layers
-===============
-
-maxid_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: maxid_layer
-    :noindex:
-
-sampling_id_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sampling_id_layer
-    :noindex:
-
-Slicing and Joining Layers
-==========================
-
-pad_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: pad_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_cost_layers:
-
-Cost Layers
-===========
-
-cross_entropy
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: cross_entropy
-    :noindex:
-
-cross_entropy_with_selfnorm
----------------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: cross_entropy_with_selfnorm
-    :noindex:
-
-multi_binary_label_cross_entropy
---------------------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :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
-    :members: huber_cost
-    :noindex:
-
-lambda_cost
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: lambda_cost
-    :noindex:
-
-rank_cost
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: rank_cost
-    :noindex:
-
-sum_cost
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sum_cost
-    :noindex:
-
-crf_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: crf_layer
-    :noindex:
-
-crf_decoding_layer
--------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: crf_decoding_layer
-    :noindex:
-
-ctc_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: ctc_layer
-    :noindex:
-
-warp_ctc_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: warp_ctc_layer
-    :noindex:
-
-nce_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: nce_layer
-    :noindex:
-
-hsigmoid
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: hsigmoid
-    :noindex:
-
-smooth_l1_cost
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: smooth_l1_cost
-    :noindex:
-
-Check Layer 
-============
-
-eos_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: eos_layer
-    :noindex:

develop/doc/_sources/api/v1/trainer_config_helpers/networks.rst.txt

-========
-Networks
-========
-
-The networks module contains pieces of neural network that combine multiple layers.
-
-NLP
-===
-
-sequence_conv_pool
-------------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: sequence_conv_pool
-    :noindex:
-
-..  _api_trainer_config_helpers_network_text_conv_pool:
-
-text_conv_pool
---------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: text_conv_pool
-    :noindex:
-
-Images
-======
-
-img_conv_bn_pool
-----------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: img_conv_bn_pool
-    :noindex:
-
-img_conv_group
---------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: img_conv_group
-    :noindex:
-
-..  _api_trainer_config_helpers_network_simple_img_conv_pool:
-
-simple_img_conv_pool
---------------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_img_conv_pool
-    :noindex:
-
-vgg_16_network
----------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: vgg_16_network
-    :noindex:
-
-Recurrent
-=========
-
-LSTM
-----
-
-lstmemory_unit
-``````````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: lstmemory_unit
-    :noindex:
-
-lstmemory_group
-```````````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: lstmemory_group
-    :noindex:
-
-simple_lstm
-```````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_lstm
-    :noindex:
-
-bidirectional_lstm
-``````````````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: bidirectional_lstm
-    :noindex:
-
-GRU
----
-
-gru_unit
-````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: gru_unit
-    :noindex:
-
-gru_group
-`````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: gru_group
-    :noindex:
-
-simple_gru
-``````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_gru
-    :noindex:
-
-simple_attention
-----------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_attention
-    :noindex:
-
-Miscs
-=====
-
-dropout_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: dropout_layer
-    :noindex:
-
-outputs
--------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: outputs
-    :noindex:

develop/doc/_sources/api/v1/trainer_config_helpers/optimizers.rst.txt

-..  _api_trainer_config_helpers_optimizers:
-
-==========
-Optimizers
-==========
-
-BaseSGDOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: BaseSGDOptimizer
-    :noindex:
-
-MomentumOptimizer
-=================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: MomentumOptimizer
-    :noindex:
-
-AdamOptimizer
-=============
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdamOptimizer
-    :noindex:
-
-AdamaxOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdamaxOptimizer
-    :noindex:
-
-AdaGradOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdaGradOptimizer
-    :noindex:
-
-DecayedAdaGradOptimizer
-=======================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: DecayedAdaGradOptimizer
-    :noindex:
-
-AdaDeltaOptimizer
-=================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdaDeltaOptimizer
-    :noindex:
-
-RMSPropOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: RMSPropOptimizer
-    :noindex:
-
-..  _api_trainer_config_helpers_optimizers_settings:
-
-settings
-========
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: settings
-    :noindex:

develop/doc/_sources/api/v1/trainer_config_helpers/poolings.rst.txt

-========
-Poolings
-========
-
-BasePoolingType
-===============
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: BasePoolingType
-    :noindex:
-
-AvgPooling
-==========
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: AvgPooling
-    :noindex:
-
-MaxPooling
-==========
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: MaxPooling
-    :noindex:
-
-SumPooling
-==========
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: SumPooling
-    :noindex:
-
-SquareRootNPooling
-==================
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: SquareRootNPooling
-    :noindex:

develop/doc/_sources/api/v2/config/evaluators.rst.txt

+..  _api_v2:
+
+==========
+Evaluators
+==========
+
+Classification 
+==============
+
+classification_error
+--------------------
+..  automodule:: paddle.v2.evaluator
+    :members: classification_error
+    :noindex:
+
+auc
+---
+..  automodule:: paddle.v2.evaluator
+    :members: auc
+    :noindex:
+
+ctc_error
+---------
+..  automodule:: paddle.v2.evaluator
+    :members: ctc_error
+    :noindex:
+
+chunk
+-----
+..  automodule:: paddle.v2.evaluator
+    :members: chunk
+    :noindex:
+
+precision_recall
+----------------
+..  automodule:: paddle.v2.evaluator
+    :members:  precision_recall
+    :noindex:
+
+Rank
+====
+
+pnpair
+------
+..  automodule:: paddle.v2.evaluator
+    :members:  pnpair
+    :noindex:
+
+Utils
+=====
+
+sum
+---
+..  automodule:: paddle.v2.evaluator
+    :members: sum
+    :noindex:
+
+column_sum
+----------
+..  automodule:: paddle.v2.evaluator
+    :members: column_sum
+    :noindex:
+
+Print
+=====
+
+classification_error_printer
+----------------------------
+..  automodule:: paddle.v2.evaluator
+    :members:  classification_error_printer
+    :noindex:
+
+gradient_printer
+----------------
+..  automodule:: paddle.v2.evaluator
+    :members:  gradient_printer
+    :noindex:
+
+maxid_printer
+-------------
+..  automodule:: paddle.v2.evaluator
+    :members:  maxid_printer
+    :noindex:
+
+maxframe_printer
+----------------
+..  automodule:: paddle.v2.evaluator
+    :members:  maxframe_printer
+    :noindex:
+
+seqtext_printer
+---------------
+..  automodule:: paddle.v2.evaluator
+    :members:  seqtext_printer
+    :noindex:
+
+value_printer
+-------------
+..  automodule:: paddle.v2.evaluator
+    :members:  value_printer
+    :noindex:

develop/doc/_sources/api/v2/config/networks.rst.txt

@@ -44,6 +44,12 @@ simple_img_conv_pool
     :members: simple_img_conv_pool
     :noindex:
 
+small_vgg
+---------
+..  automodule:: paddle.v2.networks
+    :members: small_vgg
+    :noindex:
+
 vgg_16_network
 ---------------
 ..  automodule:: paddle.v2.networks
@@ -101,6 +107,18 @@ simple_gru
     :members: simple_gru
     :noindex:
 
+simple_gru2
+```````````
+..  automodule:: paddle.v2.networks
+    :members: simple_gru2
+    :noindex:
+
+bidirectional_gru
+``````````````````
+..  automodule:: paddle.v2.networks
+    :members: bidirectional_gru
+    :noindex:
+
 simple_attention
 ----------------
 ..  automodule:: paddle.v2.networks

develop/doc/_sources/api/v2/model_configs.rst.txt

@@ -6,6 +6,7 @@ Model Configuration
 
     config/activation.rst
     config/layer.rst
+    config/evaluators.rst
     config/optimizer.rst
     config/pooling.rst
     config/networks.rst

develop/doc/about/index_en.html

@@ -136,6 +136,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc/api/index_en.html

@@ -137,6 +137,7 @@
 <li class="toctree-l2"><a class="reference internal" href="v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/networks.html">Networks</a></li>

develop/doc/api/v1/data_provider/dataprovider_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>

develop/doc/api/v1/data_provider/pydataprovider2_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>

develop/doc/api/v1/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/networks.html">Networks</a></li>
@@ -189,16 +190,6 @@
 <div class="section" id="model-config-api">
 <span id="api-trainer-config"></span><h2>Model Config API<a class="headerlink" href="#model-config-api" title="Permalink to this headline">¶</a></h2>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/optimizers.html">Optimizers</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/data_sources.html">DataSources</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/layers.html">Layers</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/activations.html">Activations</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/poolings.html">Poolings</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/networks.html">Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/evaluators.html">Evaluators</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/attrs.html">Parameter Attributes</a></li>
-</ul>
 </div>
 </div>
 <div class="section" id="applications-api">

develop/doc/api/v1/predict/swig_py_paddle_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>

develop/doc/api/v1/trainer_config_helpers/activations.html

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-            
-  <div class="section" id="activations">
-<h1>Activations<a class="headerlink" href="#activations" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="baseactivation">
-<h2>BaseActivation<a class="headerlink" href="#baseactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">BaseActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Base</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="absactivation">
-<h2>AbsActivation<a class="headerlink" href="#absactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">AbsActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Abs</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="expactivation">
-<h2>ExpActivation<a class="headerlink" href="#expactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">ExpActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Exp</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="identityactivation">
-<h2>IdentityActivation<a class="headerlink" href="#identityactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">IdentityActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Linear</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="linearactivation">
-<h2>LinearActivation<a class="headerlink" href="#linearactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">LinearActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Linear</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="logactivation">
-<h2>LogActivation<a class="headerlink" href="#logactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">LogActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Log</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="squareactivation">
-<h2>SquareActivation<a class="headerlink" href="#squareactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SquareActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Square</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="sigmoidactivation">
-<h2>SigmoidActivation<a class="headerlink" href="#sigmoidactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SigmoidActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Sigmoid</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="softmaxactivation">
-<h2>SoftmaxActivation<a class="headerlink" href="#softmaxactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SoftmaxActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Softmax</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="sequencesoftmaxactivation">
-<h2>SequenceSoftmaxActivation<a class="headerlink" href="#sequencesoftmaxactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SequenceSoftmaxActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">SequenceSoftmax</span></code></p>
-</dd></dl>
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-</div>
-<div class="section" id="reluactivation">
-<h2>ReluActivation<a class="headerlink" href="#reluactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">ReluActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Relu</span></code></p>
-</dd></dl>
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-</div>
-<div class="section" id="breluactivation">
-<h2>BReluActivation<a class="headerlink" href="#breluactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">BReluActivation</code></dt>
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-</dd></dl>
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-</div>
-<div class="section" id="softreluactivation">
-<h2>SoftReluActivation<a class="headerlink" href="#softreluactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SoftReluActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">SoftRelu</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="tanhactivation">
-<h2>TanhActivation<a class="headerlink" href="#tanhactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">TanhActivation</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Tanh</span></code></p>
-</dd></dl>
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-</div>
-<div class="section" id="stanhactivation">
-<h2>STanhActivation<a class="headerlink" href="#stanhactivation" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">STanhActivation</code></dt>
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-  <div class="section" id="module-paddle.trainer_config_helpers.attrs">
-<span id="parameter-attributes"></span><h1>Parameter Attributes<a class="headerlink" href="#module-paddle.trainer_config_helpers.attrs" title="Permalink to this headline">¶</a></h1>
-<dl class="class">
-<dt id="paddle.trainer_config_helpers.attrs.ParameterAttribute">
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ParameterAttribute</code><span class="sig-paren">(</span><em>name=None</em>, <em>is_static=False</em>, <em>initial_std=None</em>, <em>initial_mean=None</em>, <em>initial_max=None</em>, <em>initial_min=None</em>, <em>l1_rate=None</em>, <em>l2_rate=None</em>, <em>learning_rate=None</em>, <em>momentum=None</em>, <em>gradient_clipping_threshold=None</em>, <em>sparse_update=False</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="Permalink to this definition">¶</a></dt>
-<dd><p>Parameter Attributes object. To fine-tuning network training process, user
-can set attribute to control training details, such as l1,l2 rate / learning
-rate / how to init param.</p>
-<p>NOTE: IT IS A HIGH LEVEL USER INTERFACE.</p>
-<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 last simple">
-<li><strong>is_static</strong> (<em>bool</em>) &#8211; True if this parameter will be fixed while training.</li>
-<li><strong>initial_std</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Gauss Random initialization standard deviation.
-None if not using Gauss Random initialize parameter.</li>
-<li><strong>initial_mean</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Gauss Random initialization mean.
-None if not using Gauss Random initialize parameter.</li>
-<li><strong>initial_max</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Uniform initialization max value.</li>
-<li><strong>initial_min</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Uniform initialization min value.</li>
-<li><strong>l1_rate</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; the l1 regularization factor</li>
-<li><strong>l2_rate</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; the l2 regularization factor</li>
-<li><strong>learning_rate</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; The parameter learning rate. None means 1.
-The learning rate when optimize is LEARNING_RATE =
-GLOBAL_LEARNING_RATE * PARAMETER_LEARNING_RATE
-* SCHEDULER_FACTOR.</li>
-<li><strong>momentum</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; The parameter momentum. None means use global value.</li>
-<li><strong>gradient_clipping_threshold</strong> (<em>float</em>) &#8211; gradient clipping threshold. If gradient
-value larger than some value, will be
-clipped.</li>
-<li><strong>sparse_update</strong> (<em>bool</em>) &#8211; Enable sparse update for this parameter. It will
-enable both local and remote sparse update.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="paddle.trainer_config_helpers.attrs.ParameterAttribute.set_default_parameter_name">
-<code class="descname">set_default_parameter_name</code><span class="sig-paren">(</span><em>name</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ParameterAttribute.set_default_parameter_name" title="Permalink to this definition">¶</a></dt>
-<dd><p>Set default parameter name. If parameter not set, then will use default
-parameter name.</p>
-<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"><strong>name</strong> (<em>basestring</em>) &#8211; default parameter name.</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute">
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ExtraLayerAttribute</code><span class="sig-paren">(</span><em>error_clipping_threshold=None</em>, <em>drop_rate=None</em>, <em>device=None</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="Permalink to this definition">¶</a></dt>
-<dd><p>Some high level layer attributes config. You can set all attributes here,
-but some layer doesn&#8217;t support all attributes. If you set an attribute to a
-layer that not support this attribute, paddle will print an error and core.</p>
-<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 last simple">
-<li><strong>error_clipping_threshold</strong> (<em>float</em>) &#8211; Error clipping threshold.</li>
-<li><strong>drop_rate</strong> (<em>float</em>) &#8211; Dropout rate. Dropout will create a mask on layer output.
-The dropout rate is the zero rate of this mask. The
-details of what dropout is please refer to <a class="reference external" href="https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf">here</a>.</li>
-<li><strong>device</strong> (<em>int</em>) &#8211; <p>device ID of layer. device=-1, use CPU. device&gt;=0, use GPU.
-The details allocation in parallel_nn please refer to <a class="reference external" href="http://www.paddlepaddle.org/doc/ui/cmd_argument/use_case.html#case-2-specify-layers-in-different-devices">here</a>.</p>
-</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="paddle.trainer_config_helpers.attrs.ParamAttr">
-<code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ParamAttr</code><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ParamAttr" title="Permalink to this definition">¶</a></dt>
-<dd><p>alias of <a class="reference internal" href="#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><code class="xref py py-class docutils literal"><span class="pre">ParameterAttribute</span></code></a></p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="paddle.trainer_config_helpers.attrs.ExtraAttr">
-<code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ExtraAttr</code><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ExtraAttr" title="Permalink to this definition">¶</a></dt>
-<dd><p>alias of <a class="reference internal" href="#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><code class="xref py py-class docutils literal"><span class="pre">ExtraLayerAttribute</span></code></a></p>
-</dd></dl>
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-  <div class="section" id="module-paddle.trainer_config_helpers.data_sources">
-<span id="datasources"></span><span id="api-trainer-config-helpers-data-sources"></span><h1>DataSources<a class="headerlink" href="#module-paddle.trainer_config_helpers.data_sources" title="Permalink to this headline">¶</a></h1>
-<p>Data Sources are helpers to define paddle training data or testing data.</p>
-<dl class="function">
-<dt id="paddle.trainer_config_helpers.data_sources.define_py_data_sources2">
-<code class="descclassname">paddle.trainer_config_helpers.data_sources.</code><code class="descname">define_py_data_sources2</code><span class="sig-paren">(</span><em>train_list</em>, <em>test_list</em>, <em>module</em>, <em>obj</em>, <em>args=None</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.data_sources.define_py_data_sources2" title="Permalink to this definition">¶</a></dt>
-<dd><p>Define python Train/Test data sources in one method. If train/test use
-the same Data Provider configuration, module/obj/args contain one argument,
-otherwise contain a list or tuple of arguments. For example:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">define_py_data_sources2</span><span class="p">(</span><span class="n">train_list</span><span class="o">=</span><span class="s2">&quot;train.list&quot;</span><span class="p">,</span>
-                        <span class="n">test_list</span><span class="o">=</span><span class="s2">&quot;test.list&quot;</span><span class="p">,</span>
-                        <span class="n">module</span><span class="o">=</span><span class="s2">&quot;data_provider&quot;</span>
-                        <span class="c1"># if train/test use different configurations,</span>
-                        <span class="c1"># obj=[&quot;process_train&quot;, &quot;process_test&quot;]</span>
-                        <span class="n">obj</span><span class="o">=</span><span class="s2">&quot;process&quot;</span><span class="p">,</span>
-                        <span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;dictionary&quot;</span><span class="p">:</span> <span class="n">dict_name</span><span class="p">})</span>
-</pre></div>
-</div>
-<p>The related data provider can refer to <a class="reference internal" href="../data_provider/pydataprovider2_en.html#api-pydataprovider2-sequential-model"><span class="std std-ref">DataProvider for the sequential model</span></a> .</p>
-<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>train_list</strong> (<em>basestring</em>) &#8211; Train list name.</li>
-<li><strong>test_list</strong> (<em>basestring</em>) &#8211; Test list name.</li>
-<li><strong>module</strong> (<em>basestring</em><em> or </em><em>tuple</em><em> or </em><em>list</em>) &#8211; python module name. If train and test is different, then
-pass a tuple or list to this argument.</li>
-<li><strong>obj</strong> (<em>basestring</em><em> or </em><em>tuple</em><em> or </em><em>list</em>) &#8211; python object name. May be a function name if using
-PyDataProviderWrapper. If train and test is different, then pass
-a tuple or list to this argument.</li>
-<li><strong>args</strong> (<em>string</em><em> or </em><em>picklable object</em><em> or </em><em>list</em><em> or </em><em>tuple.</em>) &#8211; The best practice is using dict() to pass arguments into
-DataProvider, and use <code class="code docutils literal"><span class="pre">&#64;init_hook_wrapper</span></code> to receive
-arguments. If train and test is different, then pass a tuple
-or list to this argument.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">None</p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">None</p>
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-  <div class="section" id="networks">
-<h1>Networks<a class="headerlink" href="#networks" title="Permalink to this headline">¶</a></h1>
-<p>The networks module contains pieces of neural network that combine multiple layers.</p>
-<div class="section" id="nlp">
-<h2>NLP<a class="headerlink" href="#nlp" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="sequence-conv-pool">
-<h3>sequence_conv_pool<a class="headerlink" href="#sequence-conv-pool" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">sequence_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Text convolution pooling layers helper.</p>
-<p>Text input =&gt; Context Projection =&gt; FC Layer =&gt; Pooling =&gt; Output.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; name of output layer(pooling layer name)</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; name of input layer</li>
-<li><strong>context_len</strong> (<em>int</em>) &#8211; context projection length. See
-context_projection&#8217;s document.</li>
-<li><strong>hidden_size</strong> (<em>int</em>) &#8211; FC Layer size.</li>
-<li><strong>context_start</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; context projection length. See
-context_projection&#8217;s context_start.</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType.</em>) &#8211; pooling layer type. See pooling_layer&#8217;s document.</li>
-<li><strong>context_proj_layer_name</strong> (<em>basestring</em>) &#8211; context projection layer name.
-None if user don&#8217;t care.</li>
-<li><strong>context_proj_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; context projection parameter attribute.
-None if user don&#8217;t care.</li>
-<li><strong>fc_layer_name</strong> (<em>basestring</em>) &#8211; fc layer name. None if user don&#8217;t care.</li>
-<li><strong>fc_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
-<li><strong>fc_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc bias parameter attribute. False if no bias,
-None if user don&#8217;t care.</li>
-<li><strong>fc_act</strong> (<em>BaseActivation</em>) &#8211; fc layer activation type. None means tanh</li>
-<li><strong>pool_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
-False if no bias.</li>
-<li><strong>fc_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; fc layer extra attribute.</li>
-<li><strong>context_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; context projection layer extra attribute.</li>
-<li><strong>pool_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; pooling layer extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">output layer name.</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="text-conv-pool">
-<span id="api-trainer-config-helpers-network-text-conv-pool"></span><h3>text_conv_pool<a class="headerlink" href="#text-conv-pool" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">text_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Text convolution pooling layers helper.</p>
-<p>Text input =&gt; Context Projection =&gt; FC Layer =&gt; Pooling =&gt; Output.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; name of output layer(pooling layer name)</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; name of input layer</li>
-<li><strong>context_len</strong> (<em>int</em>) &#8211; context projection length. See
-context_projection&#8217;s document.</li>
-<li><strong>hidden_size</strong> (<em>int</em>) &#8211; FC Layer size.</li>
-<li><strong>context_start</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; context projection length. See
-context_projection&#8217;s context_start.</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType.</em>) &#8211; pooling layer type. See pooling_layer&#8217;s document.</li>
-<li><strong>context_proj_layer_name</strong> (<em>basestring</em>) &#8211; context projection layer name.
-None if user don&#8217;t care.</li>
-<li><strong>context_proj_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; context projection parameter attribute.
-None if user don&#8217;t care.</li>
-<li><strong>fc_layer_name</strong> (<em>basestring</em>) &#8211; fc layer name. None if user don&#8217;t care.</li>
-<li><strong>fc_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
-<li><strong>fc_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc bias parameter attribute. False if no bias,
-None if user don&#8217;t care.</li>
-<li><strong>fc_act</strong> (<em>BaseActivation</em>) &#8211; fc layer activation type. None means tanh</li>
-<li><strong>pool_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
-False if no bias.</li>
-<li><strong>fc_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; fc layer extra attribute.</li>
-<li><strong>context_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; context projection layer extra attribute.</li>
-<li><strong>pool_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; pooling layer extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">output layer name.</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="images">
-<h2>Images<a class="headerlink" href="#images" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="img-conv-bn-pool">
-<h3>img_conv_bn_pool<a class="headerlink" href="#img-conv-bn-pool" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">img_conv_bn_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Convolution, batch normalization, pooling group.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; group name</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; layer&#8217;s input</li>
-<li><strong>filter_size</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
-<li><strong>num_filters</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
-<li><strong>pool_size</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; see batch_norm_layer&#8217;s document.</li>
-<li><strong>groups</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
-<li><strong>conv_stride</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_padding</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>num_channel</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>shared_bias</strong> (<em>bool</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_layer_attr</strong> (<em>ExtraLayerOutput</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>bn_param_attr</strong> (<em>ParameterAttribute.</em>) &#8211; see batch_norm_layer&#8217;s document.</li>
-<li><strong>bn_bias_attr</strong> &#8211; see batch_norm_layer&#8217;s document.</li>
-<li><strong>bn_layer_attr</strong> &#8211; ParameterAttribute.</li>
-<li><strong>pool_stride</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>pool_padding</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>pool_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; see img_pool_layer&#8217;s document.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Layer groups output</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="img-conv-group">
-<h3>img_conv_group<a class="headerlink" href="#img-conv-group" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">img_conv_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Image Convolution Group, Used for vgg net.</p>
-<p>TODO(yuyang18): Complete docs</p>
-<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>conv_batchnorm_drop_rate</strong> &#8211; </li>
-<li><strong>input</strong> &#8211; </li>
-<li><strong>conv_num_filter</strong> &#8211; </li>
-<li><strong>pool_size</strong> &#8211; </li>
-<li><strong>num_channels</strong> &#8211; </li>
-<li><strong>conv_padding</strong> &#8211; </li>
-<li><strong>conv_filter_size</strong> &#8211; </li>
-<li><strong>conv_act</strong> &#8211; </li>
-<li><strong>conv_with_batchnorm</strong> &#8211; </li>
-<li><strong>pool_stride</strong> &#8211; </li>
-<li><strong>pool_type</strong> &#8211; </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="simple-img-conv-pool">
-<span id="api-trainer-config-helpers-network-simple-img-conv-pool"></span><h3>simple_img_conv_pool<a class="headerlink" href="#simple-img-conv-pool" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_img_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Simple image convolution and pooling group.</p>
-<p>Input =&gt; conv =&gt; pooling</p>
-<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>name</strong> (<em>basestring</em>) &#8211; group name</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
-<li><strong>filter_size</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>num_filters</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>pool_size</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>groups</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>conv_stride</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>conv_padding</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer for details</li>
-<li><strong>num_channel</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer for details</li>
-<li><strong>shared_bias</strong> (<em>bool</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>conv_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; see img_conv_layer for details</li>
-<li><strong>pool_stride</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>pool_padding</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>pool_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; see img_pool_layer for details</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Layer&#8217;s output</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="vgg-16-network">
-<h3>vgg_16_network<a class="headerlink" href="#vgg-16-network" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">vgg_16_network</code><span class="sig-paren">(</span><em>input_image</em>, <em>num_channels</em>, <em>num_classes=1000</em><span class="sig-paren">)</span></dt>
-<dd><p>Same model from <a class="reference external" href="https://gist.github.com/ksimonyan/211839e770f7b538e2d8">https://gist.github.com/ksimonyan/211839e770f7b538e2d8</a></p>
-<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>num_classes</strong> &#8211; </li>
-<li><strong>input_image</strong> (<em>LayerOutput</em>) &#8211; </li>
-<li><strong>num_channels</strong> (<em>int</em>) &#8211; </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-</div>
-<div class="section" id="recurrent">
-<h2>Recurrent<a class="headerlink" href="#recurrent" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="lstm">
-<h3>LSTM<a class="headerlink" href="#lstm" title="Permalink to this headline">¶</a></h3>
-<div class="section" id="lstmemory-unit">
-<h4>lstmemory_unit<a class="headerlink" href="#lstmemory-unit" title="Permalink to this headline">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">lstmemory_unit</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Define calculations that a LSTM unit performs in a single time step.
-This function itself is not a recurrent layer, so that it can not be
-directly applied to sequence input. This function is always used in
-recurrent_group (see layers.py for more details) to implement attention
-mechanism.</p>
-<p>Please refer to  <strong>Generating Sequences With Recurrent Neural Networks</strong>
-for more details about LSTM. The link goes as follows:
-.. _Link: <a class="reference external" href="https://arxiv.org/abs/1308.0850">https://arxiv.org/abs/1308.0850</a></p>
-<div class="math">
-\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">lstm_step</span> <span class="o">=</span> <span class="n">lstmemory_unit</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span>
-                           <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
-                           <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                           <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
-                           <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; lstmemory unit name.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstmemory unit size.</li>
-<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
-<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
-<li><strong>mixed_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of mixed layer.
-False means no bias, None means default bias.</li>
-<li><strong>lstm_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of lstm layer.
-False means no bias, None means default bias.</li>
-<li><strong>mixed_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; mixed layer&#8217;s extra attribute.</li>
-<li><strong>lstm_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; lstm layer&#8217;s extra attribute.</li>
-<li><strong>get_output_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; get output layer&#8217;s extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">lstmemory unit name.</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="lstmemory-group">
-<h4>lstmemory_group<a class="headerlink" href="#lstmemory-group" title="Permalink to this headline">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">lstmemory_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>lstm_group is a recurrent layer group version of Long Short Term Memory. It
-does exactly the same calculation as the lstmemory layer (see lstmemory in
-layers.py for the maths) does. A promising benefit is that LSTM memory
-cell states, or hidden states in every time step are accessible to the
-user. This is especially useful in attention model. If you do not need to
-access the internal states of the lstm, but merely use its outputs,
-it is recommended to use the lstmemory, which is relatively faster than
-lstmemory_group.</p>
-<p>NOTE: In PaddlePaddle&#8217;s implementation, the following input-to-hidden
-multiplications:
-<span class="math">\(W_{xi}x_{t}\)</span> , <span class="math">\(W_{xf}x_{t}\)</span>,
-<span class="math">\(W_{xc}x_t\)</span>, <span class="math">\(W_{xo}x_{t}\)</span> are not done in lstmemory_unit to
-speed up the calculations. Consequently, an additional mixed_layer with
-full_matrix_projection must be included before lstmemory_unit is called.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">lstm_step</span> <span class="o">=</span> <span class="n">lstmemory_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span>
-                            <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
-                            <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                            <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
-                            <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; lstmemory group name.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstmemory group size.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; is lstm reversed</li>
-<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
-<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
-<li><strong>mixed_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of mixed layer.
-False means no bias, None means default bias.</li>
-<li><strong>lstm_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of lstm layer.
-False means no bias, None means default bias.</li>
-<li><strong>mixed_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; mixed layer&#8217;s extra attribute.</li>
-<li><strong>lstm_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; lstm layer&#8217;s extra attribute.</li>
-<li><strong>get_output_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; get output layer&#8217;s extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">the lstmemory group.</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="simple-lstm">
-<h4>simple_lstm<a class="headerlink" href="#simple-lstm" title="Permalink to this headline">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_lstm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Simple LSTM Cell.</p>
-<p>It just combine a mixed layer with fully_matrix_projection and a lstmemory
-layer. The simple lstm cell was implemented as follow equations.</p>
-<div class="math">
-\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
-<p>Please refer <strong>Generating Sequences With Recurrent Neural Networks</strong> if you
-want to know what lstm is. <a class="reference external" href="http://arxiv.org/abs/1308.0850">Link</a> is here.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; lstm layer name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstm layer size.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
-<li><strong>mat_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; mixed layer&#8217;s matrix projection parameter attribute.</li>
-<li><strong>bias_param_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute. False means no bias, None
-means default bias.</li>
-<li><strong>inner_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; lstm cell parameter attribute.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
-<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
-<li><strong>mixed_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; mixed layer&#8217;s extra attribute.</li>
-<li><strong>lstm_cell_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; lstm layer&#8217;s extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">lstm layer name.</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="bidirectional-lstm">
-<h4>bidirectional_lstm<a class="headerlink" href="#bidirectional-lstm" title="Permalink to this headline">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">bidirectional_lstm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>A bidirectional_lstm is a recurrent unit that iterates over the input
-sequence both in forward and bardward orders, and then concatenate two
-outputs form a final output. However, concatenation of two outputs
-is not the only way to form the final output, you can also, for example,
-just add them together.</p>
-<p>Please refer to  <strong>Neural Machine Translation by Jointly Learning to Align
-and Translate</strong> for more details about the bidirectional lstm.
-The link goes as follows:
-.. _Link: <a class="reference external" href="https://arxiv.org/pdf/1409.0473v3.pdf">https://arxiv.org/pdf/1409.0473v3.pdf</a></p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">bi_lstm</span> <span class="o">=</span> <span class="n">bidirectional_lstm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</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>name</strong> (<em>basestring</em>) &#8211; bidirectional lstm layer name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstm layer size.</li>
-<li><strong>return_seq</strong> (<em>bool</em>) &#8211; If set False, outputs of the last time step are
-concatenated and returned.
-If set True, the entire output sequences that are
-processed in forward and backward directions are
-concatenated and returned.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">LayerOutput object accroding to the return_seq.</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="gru">
-<h3>GRU<a class="headerlink" href="#gru" title="Permalink to this headline">¶</a></h3>
-<div class="section" id="gru-unit">
-<h4>gru_unit<a class="headerlink" href="#gru-unit" title="Permalink to this headline">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">gru_unit</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Define calculations that a gated recurrent unit performs in a single time
-step. This function itself is not a recurrent layer, so that it can not be
-directly applied to sequence input. This function is almost always used in
-the recurrent_group (see layers.py for more details) to implement attention
-mechanism.</p>
-<p>Please see grumemory in layers.py for the details about the maths.</p>
-<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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activation</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activation</li>
-<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">the gru output layer.</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="gru-group">
-<h4>gru_group<a class="headerlink" href="#gru-group" title="Permalink to this headline">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">gru_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>gru_group is a recurrent layer group version of Gated Recurrent Unit. It
-does exactly the same calculation as the grumemory layer does. A promising
-benefit is that gru hidden states are accessible to the user. This is
-especially useful in attention model. If you do not need to access
-any internal state, but merely use the outputs of a GRU, it is recommended
-to use the grumemory, which is relatively faster.</p>
-<p>Please see grumemory in layers.py for more detail about the maths.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">gur_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span>
-                <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
-                <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activiation</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activiation</li>
-<li><strong>gru_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
-<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">the gru group.</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="simple-gru">
-<h4>simple_gru<a class="headerlink" href="#simple-gru" title="Permalink to this headline">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_gru</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group,
-simple_gru in network.py. The reason why there are so many interfaces is
-that we have two ways to implement recurrent neural network. One way is to
-use one complete layer to implement rnn (including simple rnn, gru and lstm)
-with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But,
-the multiplication operation <span class="math">\(W x_t\)</span> is not computed in these layers.
-See details in their interfaces in layers.py.
-The other implementation is to use an recurrent group which can ensemble a
-series of layers to compute rnn step by step. This way is flexible for
-attenion mechanism or other complex connections.</p>
-<ul class="simple">
-<li>gru_step_layer: only compute rnn by one step. It needs an memory as input
-and can be used in recurrent group.</li>
-<li>gru_unit: a wrapper of gru_step_layer with memory.</li>
-<li>gru_group: a GRU cell implemented by a combination of multiple layers in
-recurrent group.
-But <span class="math">\(W x_t\)</span> is not done in group.</li>
-<li>gru_memory: a GRU cell implemented by one layer, which does same calculation
-with gru_group and is faster than gru_group.</li>
-<li>simple_gru: a complete GRU implementation inlcuding <span class="math">\(W x_t\)</span> and
-gru_group. <span class="math">\(W\)</span> contains <span class="math">\(W_r\)</span>, <span class="math">\(W_z\)</span> and <span class="math">\(W\)</span>, see
-formula in grumemory.</li>
-</ul>
-<p>The computational speed is that, grumemory is relatively better than
-gru_group, and gru_group is relatively better than simple_gru.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">simple_gru</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activiation</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activiation</li>
-<li><strong>gru_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
-<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">the gru group.</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="simple-attention">
-<h3>simple_attention<a class="headerlink" href="#simple-attention" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_attention</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Calculate and then return a context vector by attention machanism.
-Size of the context vector equals to size of the encoded_sequence.</p>
-<div class="math">
-\[ \begin{align}\begin{aligned}a(s_{i-1},h_{j}) &amp; = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})\\e_{i,j} &amp; = a(s_{i-1}, h_{j})\\a_{i,j} &amp; = \frac{exp(e_{i,j})}{\sum_{k=1}^{T_x}{exp(e_{i,k})}}\\c_{i} &amp; = \sum_{j=1}^{T_{x}}a_{i,j}h_{j}\end{aligned}\end{align} \]</div>
-<p>where <span class="math">\(h_{j}\)</span> is the jth element of encoded_sequence,
-<span class="math">\(U_{a}h_{j}\)</span> is the jth element of encoded_proj
-<span class="math">\(s_{i-1}\)</span> is decoder_state
-<span class="math">\(f\)</span> is weight_act, and is set to tanh by default.</p>
-<p>Please refer to <strong>Neural Machine Translation by Jointly Learning to
-Align and Translate</strong> for more details. The link is as follows:
-<a class="reference external" href="https://arxiv.org/abs/1409.0473">https://arxiv.org/abs/1409.0473</a>.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">context</span> <span class="o">=</span> <span class="n">simple_attention</span><span class="p">(</span><span class="n">encoded_sequence</span><span class="o">=</span><span class="n">enc_seq</span><span class="p">,</span>
-                           <span class="n">encoded_proj</span><span class="o">=</span><span class="n">enc_proj</span><span class="p">,</span>
-                           <span class="n">decoder_state</span><span class="o">=</span><span class="n">decoder_prev</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>name</strong> (<em>basestring</em>) &#8211; name of the attention model.</li>
-<li><strong>softmax_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; parameter attribute of sequence softmax
-that is used to produce attention weight</li>
-<li><strong>weight_act</strong> (<em>Activation</em>) &#8211; activation of the attention model</li>
-<li><strong>encoded_sequence</strong> (<em>LayerOutput</em>) &#8211; output of the encoder</li>
-<li><strong>encoded_proj</strong> (<em>LayerOutput</em>) &#8211; attention weight is computed by a feed forward neural
-network which has two inputs : decoder&#8217;s hidden state
-of previous time step and encoder&#8217;s output.
-encoded_proj is output of the feed-forward network for
-encoder&#8217;s output. Here we pre-compute it outside
-simple_attention for speed consideration.</li>
-<li><strong>decoder_state</strong> (<em>LayerOutput</em>) &#8211; hidden state of decoder in previous time step</li>
-<li><strong>transform_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; parameter attribute of the feed-forward
-network that takes decoder_state as inputs to
-compute attention weight.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a context vector</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-</div>
-<div class="section" id="miscs">
-<h2>Miscs<a class="headerlink" href="#miscs" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="dropout-layer">
-<h3>dropout_layer<a class="headerlink" href="#dropout-layer" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">dropout_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>&#64;TODO(yuyang18): Add comments.</p>
-<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>name</strong> &#8211; </li>
-<li><strong>input</strong> &#8211; </li>
-<li><strong>dropout_rate</strong> &#8211; </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="outputs">
-<h3>outputs<a class="headerlink" href="#outputs" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">outputs</code><span class="sig-paren">(</span><em>layers</em>, <em>*args</em><span class="sig-paren">)</span></dt>
-<dd><p>Declare the outputs of network. If user have not defined the inputs of
-network, this method will calculate the input order by dfs travel.</p>
-<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"><strong>layers</strong> (<em>list|tuple|LayerOutput</em>) &#8211; Output layers.</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
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-            
-  <div class="section" id="optimizers">
-<span id="api-trainer-config-helpers-optimizers"></span><h1>Optimizers<a class="headerlink" href="#optimizers" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="basesgdoptimizer">
-<h2>BaseSGDOptimizer<a class="headerlink" href="#basesgdoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">BaseSGDOptimizer</code></dt>
-<dd><p>SGD Optimizer.</p>
-<p>SGD is an optimization method, trying to find a neural network that
-minimize the &#8220;cost/error&#8221; of it by iteration. In paddle&#8217;s implementation
-SGD Optimizer is synchronized, which means all gradients will be wait to
-calculate and reduced into one gradient, then do optimize operation.</p>
-<p>The neural network consider the learning problem of minimizing an objective
-function, that has the form of a sum</p>
-<div class="math">
-\[Q(w) = \sum_{i}^{n} Q_i(w)\]</div>
-<p>The value of function Q sometimes is the cost of neural network (Mean
-Square Error between prediction and label for example). The function Q is
-parametrised by w, the weight/bias of neural network. And weights is what to
-be learned. The i is the i-th observation in (trainning) data.</p>
-<p>So, the SGD method will optimize the weight by</p>
-<div class="math">
-\[w = w - \eta \nabla Q(w) = w - \eta \sum_{i}^{n} \nabla Q_i(w)\]</div>
-<p>where <span class="math">\(\eta\)</span> is learning rate. And <span class="math">\(n\)</span> is batch size.</p>
-</dd></dl>
-
-</div>
-<div class="section" id="momentumoptimizer">
-<h2>MomentumOptimizer<a class="headerlink" href="#momentumoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">MomentumOptimizer</code><span class="sig-paren">(</span><em>momentum=None</em>, <em>sparse=False</em><span class="sig-paren">)</span></dt>
-<dd><p>MomentumOptimizer.</p>
-<p>When sparse=True, the update scheme:</p>
-<div class="math">
-\[\begin{split}\alpha_t &amp;= \alpha_{t-1} / k \\
-\beta_t &amp;= \beta_{t-1} / (1 + \lambda \gamma_t) \\
-u_t &amp;= u_{t-1} - \alpha_t \gamma_t g_t \\
-v_t &amp;= v_{t-1} + \tau_{t-1} \alpha_t \gamma_t g_t \\
-\tau_t &amp;= \tau_{t-1} + \beta_t / \alpha_t\end{split}\]</div>
-<p>where <span class="math">\(k\)</span> is momentum, <span class="math">\(\lambda\)</span> is decay rate,
-<span class="math">\(\gamma_t\)</span> is learning rate at the t&#8217;th step.</p>
-<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"><strong>sparse</strong> (<em>bool</em>) &#8211; with sparse support or not.</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adamoptimizer">
-<h2>AdamOptimizer<a class="headerlink" href="#adamoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdamOptimizer</code><span class="sig-paren">(</span><em>beta1=0.9</em>, <em>beta2=0.999</em>, <em>epsilon=1e-08</em><span class="sig-paren">)</span></dt>
-<dd><p>Adam optimizer.
-The details of please refer <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
-<div class="math">
-\[\begin{split}m(w, t) &amp; = \beta_1 m(w, t-1) + (1 - \beta_1) \nabla Q_i(w) \\
-v(w, t) &amp; = \beta_2 v(w, t-1) + (1 - \beta_2)(\nabla Q_i(w)) ^2 \\
-w &amp; = w - \frac{\eta}{\sqrt{v(w,t) + \epsilon}}\end{split}\]</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 last simple">
-<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in equation.</li>
-<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in equation.</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in equation. It is used to prevent
-divided by zero.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adamaxoptimizer">
-<h2>AdamaxOptimizer<a class="headerlink" href="#adamaxoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdamaxOptimizer</code><span class="sig-paren">(</span><em>beta1</em>, <em>beta2</em><span class="sig-paren">)</span></dt>
-<dd><p>Adamax optimizer.</p>
-<p>The details of please refer this <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
-<div class="math">
-\[\begin{split}m_t &amp; = \beta_1 * m_{t-1} + (1-\beta_1)* \nabla Q_i(w) \\
-u_t &amp; = max(\beta_2*u_{t-1}, abs(\nabla Q_i(w))) \\
-w_t &amp; = w_{t-1} - (\eta/(1-\beta_1^t))*m_t/u_t\end{split}\]</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 last simple">
-<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in the equation.</li>
-<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in the equation.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adagradoptimizer">
-<h2>AdaGradOptimizer<a class="headerlink" href="#adagradoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdaGradOptimizer</code></dt>
-<dd><p>Adagrad(for ADAptive GRAdient algorithm) optimizer.</p>
-<p>For details please refer this <a class="reference external" href="http://www.magicbroom.info/Papers/DuchiHaSi10.pdf">Adaptive Subgradient Methods for
-Online Learning and Stochastic Optimization</a>.</p>
-<div class="math">
-\[\begin{split}G &amp;= \sum_{\tau=1}^{t} g_{\tau} g_{\tau}^T \\
-w &amp; = w - \eta diag(G)^{-\frac{1}{2}} \circ g\end{split}\]</div>
-</dd></dl>
-
-</div>
-<div class="section" id="decayedadagradoptimizer">
-<h2>DecayedAdaGradOptimizer<a class="headerlink" href="#decayedadagradoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">DecayedAdaGradOptimizer</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em><span class="sig-paren">)</span></dt>
-<dd><p>AdaGrad method with decayed sum gradients. The equations of this method
-show as follow.</p>
-<div class="math">
-\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
-learning\_rate &amp;= 1/sqrt( ( E(g_t^2) + \epsilon )\end{split}\]</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 last simple">
-<li><strong>rho</strong> (<em>float</em>) &#8211; The <span class="math">\(\rho\)</span> parameter in that equation</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; The <span class="math">\(\epsilon\)</span> parameter in that equation.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adadeltaoptimizer">
-<h2>AdaDeltaOptimizer<a class="headerlink" href="#adadeltaoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdaDeltaOptimizer</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em><span class="sig-paren">)</span></dt>
-<dd><p>AdaDelta method. The details of adadelta please refer to this
-<a class="reference external" href="http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf">ADADELTA: AN ADAPTIVE LEARNING RATE METHOD</a>.</p>
-<div class="math">
-\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
-learning\_rate &amp;= sqrt( ( E(dx_{t-1}^2) + \epsilon ) / ( \
-                  E(g_t^2) + \epsilon ) ) \\
-E(dx_t^2) &amp;= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2\end{split}\]</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 last simple">
-<li><strong>rho</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="rmspropoptimizer">
-<h2>RMSPropOptimizer<a class="headerlink" href="#rmspropoptimizer" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">RMSPropOptimizer</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em><span class="sig-paren">)</span></dt>
-<dd><p>RMSProp(for Root Mean Square Propagation) optimizer. For details please
-refer this <a class="reference external" href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">slide</a>.</p>
-<p>The equations of this method as follows:</p>
-<div class="math">
-\[\begin{split}v(w, t) &amp; = \rho v(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2 \\
-w &amp; = w - \frac{\eta} {\sqrt{v(w,t) + \epsilon}} \nabla Q_{i}(w)\end{split}\]</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 last simple">
-<li><strong>rho</strong> (<em>float</em>) &#8211; the <span class="math">\(\rho\)</span> in the equation. The forgetting factor.</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in the equation.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="settings">
-<span id="api-trainer-config-helpers-optimizers-settings"></span><h2>settings<a class="headerlink" href="#settings" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">settings</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Set the optimization method, learning rate, batch size, and other training
-settings. The currently supported algorithms are SGD and Async-SGD.</p>
-<div class="admonition warning">
-<p class="first admonition-title">Warning</p>
-<p class="last">Note that the &#8216;batch_size&#8217; in PaddlePaddle is not equal to global
-training batch size. It represents the single training process&#8217;s batch
-size. If you use N processes to train one model, for example use three
-GPU machines, the global batch size is N*&#8217;batch_size&#8217;.</p>
-</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 last simple">
-<li><strong>batch_size</strong> (<em>int</em>) &#8211; batch size for one training process.</li>
-<li><strong>learning_rate</strong> (<em>float</em>) &#8211; learning rate for SGD</li>
-<li><strong>learning_method</strong> (<em>BaseSGDOptimizer</em>) &#8211; The extension optimization algorithms of gradient
-descent, such as momentum, adagrad, rmsprop, etc.
-Note that it should be instance with base type
-BaseSGDOptimizer.</li>
-<li><strong>regularization</strong> (<em>BaseRegularization</em>) &#8211; The regularization method.</li>
-<li><strong>is_async</strong> (<em>bool</em>) &#8211; Is Async-SGD or not. Default value is False.</li>
-<li><strong>model_average</strong> (<em>ModelAverage</em>) &#8211; Model Average Settings.</li>
-<li><strong>gradient_clipping_threshold</strong> (<em>float</em>) &#8211; gradient clipping threshold. If gradient
-value larger than some value, will be
-clipped.</li>
-</ul>
-</td>
-</tr>
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develop/doc/api/v1/trainer_config_helpers/poolings.html

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-  <div class="section" id="poolings">
-<h1>Poolings<a class="headerlink" href="#poolings" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="basepoolingtype">
-<h2>BasePoolingType<a class="headerlink" href="#basepoolingtype" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">BasePoolingType</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">BasePool</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="avgpooling">
-<h2>AvgPooling<a class="headerlink" href="#avgpooling" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">AvgPooling</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Avg</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="maxpooling">
-<h2>MaxPooling<a class="headerlink" href="#maxpooling" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">MaxPooling</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Max</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="sumpooling">
-<h2>SumPooling<a class="headerlink" href="#sumpooling" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">SumPooling</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">Sum</span></code></p>
-</dd></dl>
-
-</div>
-<div class="section" id="squarerootnpooling">
-<h2>SquareRootNPooling<a class="headerlink" href="#squarerootnpooling" title="Permalink to this headline">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">SquareRootNPooling</code></dt>
-<dd><p>alias of <code class="xref py py-class docutils literal"><span class="pre">SquareRootN</span></code></p>
-</dd></dl>
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develop/doc/api/v2/config/activation.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">Model Configuration</a><ul class="current">
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>

develop/doc/api/v2/config/attr.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">Model Configuration</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>

develop/doc/api/v1/trainer_config_helpers/evaluators.html → develop/doc/api/v2/config/evaluators.html

@@ -32,7 +32,10 @@
         <link rel="index" title="Index"
               href="../../../genindex.html"/>
         <link rel="search" title="Search" href="../../../search.html"/>
-    <link rel="top" title="PaddlePaddle  documentation" href="../../../index.html"/> 
+    <link rel="top" title="PaddlePaddle  documentation" href="../../../index.html"/>
+        <link rel="up" title="Model Configuration" href="../model_configs.html"/>
+        <link rel="next" title="Optimizer" href="optimizer.html"/>
+        <link rel="prev" title="Layers" href="layer.html"/> 
 
   <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
   <link rel="stylesheet" href="../../../_static/css/override.css" type="text/css" />
@@ -80,10 +83,10 @@
       </div>
       <div class="doc-module">
         
-        <ul>
+        <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../../getstarted/index_en.html">GET STARTED</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../howto/index_en.html">HOW TO</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../index_en.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index_en.html">API</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../about/index_en.html">ABOUT</a></li>
 </ul>
 
@@ -105,7 +108,7 @@
     <nav class="doc-menu-vertical" role="navigation">
         
           
-          <ul>
+          <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../../getstarted/index_en.html">GET STARTED</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../getstarted/build_and_install/index_en.html">Install and Build</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../getstarted/build_and_install/docker_install_en.html">PaddlePaddle in Docker Containers</a></li>
@@ -131,18 +134,19 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../howto/optimization/gpu_profiling_en.html">Tune GPU Performance</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../index_en.html">API</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">Model Configuration</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/attr.html">Parameter Attribute</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index_en.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">Model Configuration</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Evaluators</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
+<li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="attr.html">Parameter Attribute</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/data.html">Data Reader Interface and DataSets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/run_logic.html">Training and Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../data.html">Data Reader Interface and DataSets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../run_logic.html">Training and Inference</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../about/index_en.html">ABOUT</a></li>
@@ -166,6 +170,10 @@
 <div role="navigation" aria-label="breadcrumbs navigation">
   <ul class="wy-breadcrumbs">
       
+        <li><a href="../../index_en.html">API</a> > </li>
+      
+        <li><a href="../model_configs.html">Model Configuration</a> > </li>
+      
     <li>Evaluators</li>
   </ul>
 </div>
@@ -176,54 +184,14 @@
            <div itemprop="articleBody">
             
   <div class="section" id="evaluators">
-<span id="api-trainer-config-helpers-evaluators"></span><h1>Evaluators<a class="headerlink" href="#evaluators" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="base">
-<h2>Base<a class="headerlink" href="#base" title="Permalink to this headline">¶</a></h2>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">evaluator_base</code><span class="sig-paren">(</span><em>input</em>, <em>type</em>, <em>label=None</em>, <em>weight=None</em>, <em>name=None</em>, <em>chunk_scheme=None</em>, <em>num_chunk_types=None</em>, <em>classification_threshold=None</em>, <em>positive_label=None</em>, <em>dict_file=None</em>, <em>result_file=None</em>, <em>num_results=None</em>, <em>delimited=None</em>, <em>top_k=None</em>, <em>excluded_chunk_types=None</em><span class="sig-paren">)</span></dt>
-<dd><p>Evaluator will evaluate the network status while training/testing.</p>
-<p>User can use evaluator by classify/regression job. For example.</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">classify</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">evaluator</span><span class="o">=</span><span class="n">classification_error_evaluator</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>And user could define evaluator separately as follow.</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">classification_error_evaluator</span><span class="p">(</span><span class="s2">&quot;ErrorRate&quot;</span><span class="p">,</span> <span class="n">prediction</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The evaluator often contains a name parameter. It will also be printed when
-evaluating network. The printed information may look like the following.</p>
-<div class="highlight-text"><div class="highlight"><pre><span></span>Batch=200 samples=20000 AvgCost=0.679655 CurrentCost=0.662179 Eval:
-classification_error_evaluator=0.4486
-CurrentEval: ErrorRate=0.3964
-</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 last simple">
-<li><strong>input</strong> (<em>list|LayerOutput</em>) &#8211; Input layers, a object of LayerOutput or a list of
-LayerOutput.</li>
-<li><strong>label</strong> (<em>LayerOutput|None</em>) &#8211; An input layer containing the ground truth label.</li>
-<li><strong>weight</strong> (<em>LayerOutput.</em>) &#8211; An input layer which is a weight for each sample.
-Each evaluator may calculate differently to use this weight.</li>
-<li><strong>top_k</strong> (<em>int</em>) &#8211; number k in top-k error rate</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
+<span id="api-v2"></span><h1>Evaluators<a class="headerlink" href="#evaluators" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="classification">
 <h2>Classification<a class="headerlink" href="#classification" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="classification-error-evaluator">
-<h3>classification_error_evaluator<a class="headerlink" href="#classification-error-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="classification-error">
+<h3>classification_error<a class="headerlink" href="#classification-error" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">classification_error_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">classification_error</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Classification Error Evaluator. It will print error rate for classification.</p>
 <p>The classification error is:</p>
 <div class="math">
@@ -238,9 +206,9 @@ Each evaluator may calculate differently to use this weight.</li>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
 <li><strong>name</strong> (<em>basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
 <li><strong>label</strong> (<em>basestring</em>) &#8211; Label layer name.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. And will just multiply to NumOfWrongPredicts
 and NumOfAllSamples. So, the elements of weight are all one,
 then means not set weight. The larger weight it is, the more
@@ -258,11 +226,11 @@ important this sample is.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="auc-evaluator">
-<h3>auc_evaluator<a class="headerlink" href="#auc-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="auc">
+<h3>auc<a class="headerlink" href="#auc" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">auc_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">auc</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Auc Evaluator which adapts to binary classification.</p>
 <p>The simple usage:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">auc_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
@@ -274,9 +242,9 @@ important this sample is.</li>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
 <li><strong>label</strong> (<em>None|basestring</em>) &#8211; Label layer name.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1].</li>
 </ul>
 </td>
@@ -286,11 +254,11 @@ important this sample is.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="ctc-error-evaluator">
-<h3>ctc_error_evaluator<a class="headerlink" href="#ctc-error-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="ctc-error">
+<h3>ctc_error<a class="headerlink" href="#ctc-error" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">ctc_error_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">ctc_error</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This evaluator is to calculate sequence-to-sequence edit distance.</p>
 <p>The simple usage is :</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">ctc_error_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">lbl</span><span class="p">)</span>
@@ -302,9 +270,9 @@ important this sample is.</li>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer. Should be the same as the input for ctc_layer.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; input label, which is a data_layer. Should be the same as the
-label for ctc_layer</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer. Should be the same as the input for ctc.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input label, which is a data. Should be the same as the
+label for ctc</li>
 </ul>
 </td>
 </tr>
@@ -313,11 +281,11 @@ label for ctc_layer</li>
 </dd></dl>
 
 </div>
-<div class="section" id="chunk-evaluator">
-<h3>chunk_evaluator<a class="headerlink" href="#chunk-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="chunk">
+<h3>chunk<a class="headerlink" href="#chunk" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">chunk_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">chunk</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Chunk evaluator is used to evaluate segment labelling accuracy for a
 sequence. It calculates the chunk detection F1 score.</p>
 <p>A chunk is correctly detected if its beginning, end and type are correct.
@@ -348,8 +316,8 @@ The tag type for each of the scheme is shown as follows:</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The input layers.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; An input layer containing the ground truth label.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layers.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; An input layer containing the ground truth label.</li>
 <li><strong>chunk_scheme</strong> (<em>basestring</em>) &#8211; The labelling schemes support 4 types. It is one of
 &#8220;IOB&#8221;, &#8220;IOE&#8221;, &#8220;IOBES&#8221;, &#8220;plain&#8221;. It is required.</li>
 <li><strong>num_chunk_types</strong> &#8211; number of chunk types other than &#8220;other&#8221;</li>
@@ -363,11 +331,11 @@ The tag type for each of the scheme is shown as follows:</p>
 </dd></dl>
 
 </div>
-<div class="section" id="precision-recall-evaluator">
-<h3>precision_recall_evaluator<a class="headerlink" href="#precision-recall-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="precision-recall">
+<h3>precision_recall<a class="headerlink" href="#precision-recall" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">precision_recall_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">precision_recall</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>An Evaluator to calculate precision and recall, F1-score.
 It is adapt to the task with multiple labels.</p>
 <ul class="simple">
@@ -386,10 +354,10 @@ F1-score of this label.</li>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Label layer name.</li>
-<li><strong>positive_label</strong> (<em>LayerOutput.</em>) &#8211; The input label layer.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label layer name.</li>
+<li><strong>positive_label</strong> (<em>paddle.v2.config_base.Layer.</em>) &#8211; The input label layer.</li>
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. (TODO, explaination)</li>
 </ul>
 </td>
@@ -402,11 +370,11 @@ F1-score of this label.</li>
 </div>
 <div class="section" id="rank">
 <h2>Rank<a class="headerlink" href="#rank" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="pnpair-evaluator">
-<h3>pnpair_evaluator<a class="headerlink" href="#pnpair-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="pnpair">
+<h3>pnpair<a class="headerlink" href="#pnpair" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">pnpair_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">pnpair</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Positive-negative pair rate Evaluator which adapts to rank task like
 learning to rank. This evaluator must contain at least three layers.</p>
 <p>The simple usage:</p>
@@ -419,10 +387,10 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Label layer name.</li>
-<li><strong>info</strong> (<em>LayerOutput</em>) &#8211; Label layer name. (TODO, explaination)</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label layer name.</li>
+<li><strong>info</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label layer name. (TODO, explaination)</li>
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. (TODO, explaination)</li>
 </ul>
 </td>
@@ -435,11 +403,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </div>
 <div class="section" id="utils">
 <h2>Utils<a class="headerlink" href="#utils" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="sum-evaluator">
-<h3>sum_evaluator<a class="headerlink" href="#sum-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="sum">
+<h3>sum<a class="headerlink" href="#sum" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">sum_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">sum</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>An Evaluator to sum the result of input.</p>
 <p>The simple usage:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">sum_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
@@ -451,8 +419,8 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name.</li>
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. (TODO, explaination)</li>
 </ul>
 </td>
@@ -462,11 +430,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="column-sum-evaluator">
-<h3>column_sum_evaluator<a class="headerlink" href="#column-sum-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="column-sum">
+<h3>column_sum<a class="headerlink" href="#column-sum" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">column_sum_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">column_sum</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to sum the last column of input.</p>
 <p>The simple usage is:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">column_sum_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
@@ -478,7 +446,7 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name.</li>
 </ul>
 </td>
 </tr>
@@ -490,11 +458,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </div>
 <div class="section" id="print">
 <h2>Print<a class="headerlink" href="#print" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="classification-error-printer-evaluator">
-<h3>classification_error_printer_evaluator<a class="headerlink" href="#classification-error-printer-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="classification-error-printer">
+<h3>classification_error_printer<a class="headerlink" href="#classification-error-printer" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">classification_error_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">classification_error_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the classification error of each sample.</p>
 <p>The simple usage is:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">classification_error_printer_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
@@ -505,8 +473,8 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Input label layer.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input label layer.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -516,11 +484,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="gradient-printer-evaluator">
-<h3>gradient_printer_evaluator<a class="headerlink" href="#gradient-printer-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="gradient-printer">
+<h3>gradient_printer<a class="headerlink" href="#gradient-printer" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">gradient_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">gradient_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the gradient of input layers. It contains
 one or more input layers.</p>
 <p>The simple usage is:</p>
@@ -532,7 +500,7 @@ one or more input layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; One or more input layers.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; One or more input layers.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -542,11 +510,11 @@ one or more input layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="maxid-printer-evaluator">
-<h3>maxid_printer_evaluator<a class="headerlink" href="#maxid-printer-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="maxid-printer">
+<h3>maxid_printer<a class="headerlink" href="#maxid-printer" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">maxid_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">maxid_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print maximum top k values and their indexes
 of each row of input layers. It contains one or more input layers.
 k is specified by num_results.</p>
@@ -559,7 +527,7 @@ k is specified by num_results.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; Input Layer name.</li>
 <li><strong>num_results</strong> (<em>int.</em>) &#8211; This number is used to specify the top k numbers.
 It is 1 by default.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
@@ -571,11 +539,11 @@ It is 1 by default.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="maxframe-printer-evaluator">
-<h3>maxframe_printer_evaluator<a class="headerlink" href="#maxframe-printer-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="maxframe-printer">
+<h3>maxframe_printer<a class="headerlink" href="#maxframe-printer" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">maxframe_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">maxframe_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the top k frames of each input layers.
 The input layers should contain sequences info or sequences type.
 k is specified by num_results.
@@ -593,7 +561,7 @@ It contains one or more input layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; Input Layer name.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -603,11 +571,11 @@ It contains one or more input layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="seqtext-printer-evaluator">
-<h3>seqtext_printer_evaluator<a class="headerlink" href="#seqtext-printer-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="seqtext-printer">
+<h3>seqtext_printer<a class="headerlink" href="#seqtext-printer" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">seqtext_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">seqtext_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Sequence text printer will print text according to index matrix and a
 dictionary. There can be multiple input to this layer:</p>
 <p>1. If there is no id_input, the input must be a matrix containing
@@ -639,7 +607,7 @@ the sequence of indices;</p>
 <p>Typically SequenceTextPrinter layer takes output of maxid or RecurrentGroup
 with maxid (when generating) as an input.</p>
 <p>The simple usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">seqtext_printer_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">maxid_layer</span><span class="p">,</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">seqtext_printer_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">maxid</span><span class="p">,</span>
                                  <span class="n">id_input</span><span class="o">=</span><span class="n">sample_id</span><span class="p">,</span>
                                  <span class="n">dict_file</span><span class="o">=</span><span class="n">dict_file</span><span class="p">,</span>
                                  <span class="n">result_file</span><span class="o">=</span><span class="n">result_file</span><span class="p">)</span>
@@ -650,9 +618,9 @@ with maxid (when generating) as an input.</p>
 <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|list</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; Input Layer name.</li>
 <li><strong>result_file</strong> (<em>basestring</em>) &#8211; Path of the file to store the generated results.</li>
-<li><strong>id_input</strong> (<em>LayerOutput</em>) &#8211; Index of the input sequence, and the specified index will
+<li><strong>id_input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Index of the input sequence, and the specified index will
 be prited in the gereated results. This an optional
 parameter.</li>
 <li><strong>dict_file</strong> (<em>basestring</em>) &#8211; Path of dictionary. This is an optional parameter.
@@ -677,11 +645,11 @@ Default is True. No space is added if set to False.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="value-printer-evaluator">
-<h3>value_printer_evaluator<a class="headerlink" href="#value-printer-evaluator" title="Permalink to this headline">¶</a></h3>
-<dl class="function">
+<div class="section" id="value-printer">
+<h3>value_printer<a class="headerlink" href="#value-printer" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">value_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">value_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the values of input layers. It contains
 one or more input layers.</p>
 <p>The simple usage is:</p>
@@ -693,7 +661,7 @@ one or more input layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; One or more input layers.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; One or more input layers.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -711,6 +679,15 @@ one or more input layers.</p>
           </div>
           <footer>
   
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="optimizer.html" class="btn btn-neutral float-right" title="Optimizer" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+      
+      
+        <a href="layer.html" class="btn btn-neutral" title="Layers" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+      
+    </div>
+  
 
   <hr/>
 

develop/doc/api/v2/config/layer.html

@@ -34,7 +34,7 @@
         <link rel="search" title="Search" href="../../../search.html"/>
     <link rel="top" title="PaddlePaddle  documentation" href="../../../index.html"/>
         <link rel="up" title="Model Configuration" href="../model_configs.html"/>
-        <link rel="next" title="Optimizer" href="optimizer.html"/>
+        <link rel="next" title="Evaluators" href="evaluators.html"/>
         <link rel="prev" title="Activation" href="activation.html"/> 
 
   <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
@@ -138,6 +138,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">Model Configuration</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>
@@ -1453,10 +1454,10 @@ Each inputs is a projection or operator.</p>
 <li><strong>input</strong> &#8211; inputs layer. It is an optional parameter. If set,
 then this function will just return layer&#8217;s name.</li>
 <li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type.</li>
-<li><strong>bias_attr</strong> (<a class="reference internal" href="../../v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em><em> or </em><em>bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
+<li><strong>bias_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None</em><em> or </em><em>bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
 something not type of ParameterAttribute. None will get a
 default Bias.</li>
-<li><strong>layer_attr</strong> (<a class="reference internal" href="../../v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; The extra layer config. Default is None.</li>
+<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; The extra layer config. Default is None.</li>
 </ul>
 </td>
 </tr>
@@ -3470,7 +3471,7 @@ It is used by recurrent layer group.</p>
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="optimizer.html" class="btn btn-neutral float-right" title="Optimizer" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="evaluators.html" class="btn btn-neutral float-right" title="Evaluators" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="activation.html" class="btn btn-neutral" title="Activation" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>

develop/doc/api/v2/config/networks.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">Model Configuration</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Networks</a></li>
@@ -409,6 +410,9 @@ False if no bias.</li>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="small-vgg">
+<h3>small_vgg<a class="headerlink" href="#small-vgg" title="Permalink to this headline">¶</a></h3>
 </div>
 <div class="section" id="vgg-16-network">
 <h3>vgg_16_network<a class="headerlink" href="#vgg-16-network" title="Permalink to this headline">¶</a></h3>
@@ -791,6 +795,86 @@ gru_group, and gru_group is relatively better than simple_gru.</p>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="simple-gru2">
+<h4>simple_gru2<a class="headerlink" href="#simple-gru2" title="Permalink to this headline">¶</a></h4>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.networks.</code><code class="descname">simple_gru2</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>simple_gru2 is the same with simple_gru, but using grumemory instead
+Please see grumemory in layers.py for more detail about the maths.
+simple_gru2 is faster than simple_gru.</p>
+<p>The example usage is:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">simple_gru2</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</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>paddle.v2.config_base.Layer</em>) &#8211; input layer name.</li>
+<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
+<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
+<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
+<li><strong>act</strong> (<em>paddle.v2.Activation.Base</em>) &#8211; type of the activiation</li>
+<li><strong>gate_act</strong> (<em>paddle.v2.Activation.Base</em>) &#8211; type of the gate activiation</li>
+<li><strong>gru_bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
+<li><strong>gru_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">the gru group.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+<div class="section" id="bidirectional-gru">
+<h4>bidirectional_gru<a class="headerlink" href="#bidirectional-gru" title="Permalink to this headline">¶</a></h4>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.networks.</code><code class="descname">bidirectional_gru</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>A bidirectional_gru is a recurrent unit that iterates over the input
+sequence both in forward and bardward orders, and then concatenate two
+outputs to form a final output. However, concatenation of two outputs
+is not the only way to form the final output, you can also, for example,
+just add them together.</p>
+<p>The example usage is:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">bi_gru</span> <span class="o">=</span> <span class="n">bidirectional_gru</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</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>name</strong> (<em>basestring</em>) &#8211; bidirectional gru layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer.</li>
+<li><strong>size</strong> (<em>int</em>) &#8211; gru layer size.</li>
+<li><strong>return_seq</strong> (<em>bool</em>) &#8211; If set False, outputs of the last time step are
+concatenated and returned.
+If set True, the entire output sequences that are
+processed in forward and backward directions are
+concatenated and returned.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </div>
 </div>
 <div class="section" id="simple-attention">

develop/doc/api/v2/config/optimizer.html

@@ -35,7 +35,7 @@
     <link rel="top" title="PaddlePaddle  documentation" href="../../../index.html"/>
         <link rel="up" title="Model Configuration" href="../model_configs.html"/>
         <link rel="next" title="Pooling" href="pooling.html"/>
-        <link rel="prev" title="Layers" href="layer.html"/> 
+        <link rel="prev" title="Evaluators" href="evaluators.html"/> 
 
   <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
   <link rel="stylesheet" href="../../../_static/css/override.css" type="text/css" />
@@ -138,6 +138,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">Model Configuration</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>
@@ -367,7 +368,7 @@ w &amp; = w - \frac{\eta} {\sqrt{v(w,t) + \epsilon}} \nabla Q_{i}(w)\end{split}\
         <a href="pooling.html" class="btn btn-neutral float-right" title="Pooling" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="layer.html" class="btn btn-neutral" title="Layers" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="evaluators.html" class="btn btn-neutral" title="Evaluators" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   

develop/doc/api/v2/config/pooling.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">Model Configuration</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>

develop/doc/api/v2/data.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2"><a class="reference internal" href="model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/networks.html">Networks</a></li>

develop/doc/api/v2/model_configs.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/networks.html">Networks</a></li>
@@ -186,6 +187,7 @@
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l1"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/networks.html">Networks</a></li>

develop/doc/api/v2/run_logic.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2"><a class="reference internal" href="model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/networks.html">Networks</a></li>
@@ -494,8 +495,9 @@ index that reader returns.</li>
 <dd><p>Infer a neural network by given neural network output and parameters.  The
 user should pass either a batch of input data or reader method.</p>
 <p>Example usages:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">SomeData</span><span class="p">,</span>
-                      <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">outptut_layer</span><span class="o">=</span><span class="n">prediction</span><span class="p">,</span>
+                      <span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span>
+                      <span class="nb">input</span><span class="o">=</span><span class="n">SomeData</span><span class="p">)</span>
 <span class="k">print</span> <span class="n">result</span>
 </pre></div>
 </div>

develop/doc/design/api.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc/design/cluster_train/README.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/design/cluster_train/checkpointing.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/design/cluster_train/data_dispatch.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/design/multi_language_interface/00.why_plain_c.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/design/multi_language_interface/01.inference_implementation.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/design/reader/README.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/design/releasing_process.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc/genindex.html

@@ -136,6 +136,7 @@
 <li class="toctree-l2"><a class="reference internal" href="api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/networks.html">Networks</a></li>
@@ -180,58 +181,17 @@
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
- <a href="#D"><strong>D</strong></a>
- | <a href="#E"><strong>E</strong></a>
- | <a href="#P"><strong>P</strong></a>
- | <a href="#S"><strong>S</strong></a>
+ <a href="#P"><strong>P</strong></a>
  
 </div>
-<h2 id="D">D</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/data_sources.html#paddle.trainer_config_helpers.data_sources.define_py_data_sources2">define_py_data_sources2() (in module paddle.trainer_config_helpers.data_sources)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ExtraAttr">ExtraAttr (in module paddle.trainer_config_helpers.attrs)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute">ExtraLayerAttribute (class in paddle.trainer_config_helpers.attrs)</a>
-</li>
-  </ul></td>
-</tr></table>
-
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#module-paddle.trainer_config_helpers.attrs">paddle.trainer_config_helpers.attrs (module)</a>
-</li>
-      <li><a href="api/v1/trainer_config_helpers/data_sources.html#module-paddle.trainer_config_helpers.data_sources">paddle.trainer_config_helpers.data_sources (module)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParamAttr">ParamAttr (in module paddle.trainer_config_helpers.attrs)</a>
-</li>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute">ParameterAttribute (class in paddle.trainer_config_helpers.attrs)</a>
-</li>
       <li><a href="api/v1/data_provider/pydataprovider2_en.html#paddle.trainer.PyDataProvider2.provider">provider() (in module paddle.trainer.PyDataProvider2)</a>
 </li>
   </ul></td>
 </tr></table>
 
-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute.set_default_parameter_name">set_default_parameter_name() (paddle.trainer_config_helpers.attrs.ParameterAttribute method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
 
 
            </div>

develop/doc/getstarted/basic_usage/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/getstarted/build_and_install/build_from_source_en.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
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develop/doc/getstarted/build_and_install/docker_install_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/getstarted/build_and_install/index_en.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc/getstarted/build_and_install/ubuntu_install_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc/getstarted/index_en.html

@@ -137,6 +137,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
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develop/doc/howto/deep_model/rnn/index_en.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc/howto/deep_model/rnn/rnn_config_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
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@@ -247,7 +248,7 @@ Its <strong>output function</strong> simply takes <span class="math">\(x_t\)</sp
 <p>We will use the sequence to sequence model with attention as an example to demonstrate how you can configure complex recurrent neural network models. An illustration of the sequence to sequence model with attention is shown in the following figure.</p>
 <img alt="../../../_images/encoder-decoder-attention-model.png" class="align-center" src="../../../_images/encoder-decoder-attention-model.png" />
 <p>In this model, the source sequence <span class="math">\(S = \{s_1, \dots, s_T\}\)</span> is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network <span class="math">\(H_S = \{H_1, \dots, H_T\}\)</span> is called <em>encoder vector</em> The decoder is a gated recurrent neural network. When decoding each token <span class="math">\(y_t\)</span>, the gated recurrent neural network generates a set of weights <span class="math">\(W_S^t = \{W_1^t, \dots, W_T^t\}\)</span>, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token <span class="math">\(y_t\)</span>.</p>
-<p>The encoder part of the model is listed below. It calls <code class="code docutils literal"><span class="pre">grumemory</span></code> to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than <code class="code docutils literal"><span class="pre">recurrent_group</span></code>. We have implemented most of the commonly used recurrent neural network architectures, you can refer to <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers"><span class="std std-ref">Layers</span></a> for more details.</p>
+<p>The encoder part of the model is listed below. It calls <code class="code docutils literal"><span class="pre">grumemory</span></code> to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than <code class="code docutils literal"><span class="pre">recurrent_group</span></code>. We have implemented most of the commonly used recurrent neural network architectures, you can refer to <span class="xref std std-ref">api_trainer_config_helpers_layers</span> for more details.</p>
 <p>We also project the encoder vector to <code class="code docutils literal"><span class="pre">decoder_size</span></code> dimensional space, get the first instance of the backward recurrent network, and project it to <code class="code docutils literal"><span class="pre">decoder_size</span></code> dimensional space:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Define the data layer of the source sentence.</span>
 <span class="n">src_word_id</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;source_language_word&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">source_dict_dim</span><span class="p">)</span>

develop/doc/howto/dev/contribute_to_paddle_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
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develop/doc/howto/dev/new_layer_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
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develop/doc/howto/index_en.html

@@ -137,6 +137,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
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develop/doc/howto/optimization/gpu_profiling_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
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develop/doc/howto/usage/cluster/cluster_train_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc/howto/usage/cmd_parameter/arguments_en.html

@@ -138,6 +138,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">Model Configuration</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc/howto/usage/cmd_parameter/detail_introduction_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc/howto/usage/cmd_parameter/index_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc/howto/usage/cmd_parameter/use_case_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc/howto/usage/k8s/k8s_aws_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc/howto/usage/k8s/k8s_en.html

@@ -138,6 +138,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc/howto/usage/k8s/src/k8s_data/README.html

@@ -135,6 +135,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../api/v2/config/evaluators.html">Evaluators</a></li>
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develop/doc/howto/usage/k8s/src/k8s_train/README.html

@@ -135,6 +135,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../api/v2/config/evaluators.html">Evaluators</a></li>
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develop/doc/index_en.html

@@ -136,6 +136,7 @@
 <li class="toctree-l2"><a class="reference internal" href="api/v2/model_configs.html">Model Configuration</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc/search.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/embedding_model/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/gan/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/image_classification/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/imagenet_model/resnet_model_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/quick_start/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/rec/ml_dataset_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/rec/ml_regression_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>
@@ -611,14 +612,14 @@ get one user feature. Then we calculate the cosine similarity of these two
 features.</p>
 <p>In these networks, we use several APIs in <a class="reference internal" href="../../api/v1/index_en.html#api-trainer-config"><span class="std std-ref">Model Config API</span></a> . There are</p>
 <ul class="simple">
-<li>Data Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-data-layer"><span class="std std-ref">data_layer</span></a></li>
-<li>Fully Connected Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-fc-layer"><span class="std std-ref">fc_layer</span></a></li>
-<li>Embedding Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-embedding-layer"><span class="std std-ref">embedding_layer</span></a></li>
-<li>Context Projection Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-context-projection"><span class="std std-ref">context_projection</span></a></li>
-<li>Pooling Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-pooling-layer"><span class="std std-ref">pooling_layer</span></a></li>
-<li>Cosine Similarity Layer, <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-cos-sim"><span class="std std-ref">cos_sim</span></a></li>
+<li>Data Layer, <span class="xref std std-ref">api_trainer_config_helpers_layers_data_layer</span></li>
+<li>Fully Connected Layer, <span class="xref std std-ref">api_trainer_config_helpers_layers_fc_layer</span></li>
+<li>Embedding Layer, <span class="xref std std-ref">api_trainer_config_helpers_layers_embedding_layer</span></li>
+<li>Context Projection Layer, <span class="xref std std-ref">api_trainer_config_helpers_layers_context_projection</span></li>
+<li>Pooling Layer, <span class="xref std std-ref">api_trainer_config_helpers_layers_pooling_layer</span></li>
+<li>Cosine Similarity Layer, <span class="xref std std-ref">api_trainer_config_helpers_layers_cos_sim</span></li>
 <li>Text Convolution Pooling Layer, <a class="reference internal" href="../../api/v2/config/networks.html#api-trainer-config-helpers-network-text-conv-pool"><span class="std std-ref">text_conv_pool</span></a></li>
-<li>Declare Python Data Sources <a class="reference internal" href="../../api/v1/trainer_config_helpers/data_sources.html#api-trainer-config-helpers-data-sources"><span class="std std-ref">DataSources</span></a>.</li>
+<li>Declare Python Data Sources <span class="xref std std-ref">api_trainer_config_helpers_data_sources</span>.</li>
 </ul>
 </div>
 <div class="section" id="data-provider">

develop/doc/tutorials/semantic_role_labeling/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/sentiment_analysis/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc/tutorials/text_generation/index_en.html

@@ -135,6 +135,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/_sources/api/v1/trainer_config_helpers/activations.rst.txt

-===========
-Activations
-===========
-
-BaseActivation
-==============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: BaseActivation
-    :noindex:
-    
-AbsActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: AbsActivation
-    :noindex:
-    
-ExpActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: ExpActivation
-    :noindex:
-    
-IdentityActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: IdentityActivation
-    :noindex:
-    
-LinearActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: LinearActivation
-    :noindex:
-
-LogActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: LogActivation
-    :noindex:
-    
-SquareActivation
-================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SquareActivation
-    :noindex:
-    
-SigmoidActivation
-=================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SigmoidActivation
-    :noindex:
-    
-SoftmaxActivation
-=================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SoftmaxActivation
-    :noindex:
-    
-SequenceSoftmaxActivation
-=========================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SequenceSoftmaxActivation
-    :noindex:
-    
-ReluActivation
-==============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: ReluActivation
-    :noindex:
-    
-BReluActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: BReluActivation
-    :noindex:
-    
-SoftReluActivation
-==================
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: SoftReluActivation
-    :noindex:
-    
-TanhActivation
-==============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: TanhActivation
-    :noindex:
-    
-STanhActivation
-===============
-
-..  automodule:: paddle.trainer_config_helpers.activations
-    :members: STanhActivation
-    :noindex:

develop/doc_cn/_sources/api/v1/trainer_config_helpers/attrs.rst.txt

-Parameter Attributes
-=======================
-
-..  automodule:: paddle.trainer_config_helpers.attrs
-    :members:

develop/doc_cn/_sources/api/v1/trainer_config_helpers/data_sources.rst.txt

-..  _api_trainer_config_helpers_data_sources:
-
-DataSources
-===========
-
-..  automodule:: paddle.trainer_config_helpers.data_sources
-    :members:

develop/doc_cn/_sources/api/v1/trainer_config_helpers/evaluators.rst.txt

-..  _api_trainer_config_helpers_evaluators:
-
-==========
-Evaluators
-==========
-
-Base
-====
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: evaluator_base
-    :noindex:
-
-Classification 
-==============
-
-classification_error_evaluator
-------------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: classification_error_evaluator
-    :noindex:
-
-auc_evaluator
--------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: auc_evaluator
-    :noindex:
-
-ctc_error_evaluator
--------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: ctc_error_evaluator
-    :noindex:
-
-chunk_evaluator
----------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: chunk_evaluator
-    :noindex:
-
-precision_recall_evaluator
---------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  precision_recall_evaluator
-    :noindex:
-
-Rank
-====
-
-pnpair_evaluator
-----------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  pnpair_evaluator
-    :noindex:
-
-Utils
-=====
-
-sum_evaluator
--------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: sum_evaluator
-    :noindex:
-
-column_sum_evaluator
---------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members: column_sum_evaluator
-    :noindex:
-
-Print
-=====
-
-classification_error_printer_evaluator
---------------------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  classification_error_printer_evaluator
-    :noindex:
-
-gradient_printer_evaluator
---------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  gradient_printer_evaluator
-    :noindex:
-
-maxid_printer_evaluator
------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  maxid_printer_evaluator
-    :noindex:
-
-maxframe_printer_evaluator
----------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  maxframe_printer_evaluator
-    :noindex:
-
-seqtext_printer_evaluator
--------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  seqtext_printer_evaluator
-    :noindex:
-
-value_printer_evaluator
------------------------
-..  automodule:: paddle.trainer_config_helpers.evaluators
-    :members:  value_printer_evaluator
-    :noindex:
-

develop/doc_cn/_sources/api/v1/trainer_config_helpers/layers.rst.txt

-..  _api_trainer_config_helpers_layers:
-
-======
-Layers
-======
-
-Base
-======
-
-LayerType
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: LayerType
-    :noindex:
-
-LayerOutput
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: LayerOutput
-    :noindex:
-
-Data layer
-===========
-
-..  _api_trainer_config_helpers_layers_data_layer:
-
-data_layer
-----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: data_layer
-    :noindex:
-
-Fully Connected Layers
-======================
-
-..  _api_trainer_config_helpers_layers_fc_layer:
-
-fc_layer
---------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: fc_layer
-    :noindex:
-
-selective_fc_layer
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: selective_fc_layer
-    :noindex:
-
-Conv Layers
-===========
-
-conv_operator
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: conv_operator
-    :noindex:
-
-conv_projection
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: conv_projection
-    :noindex:
-
-conv_shift_layer
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: conv_shift_layer
-    :noindex:
-
-img_conv_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: img_conv_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_context_projection:
-
-context_projection 
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: context_projection
-    :noindex:
-
-Image Pooling Layer
-===================
-
-img_pool_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: img_pool_layer
-    :noindex:   
-
-spp_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: spp_layer
-    :noindex:
-
-maxout_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: maxout_layer
-    :noindex:
-
-Norm Layer
-==========
-
-img_cmrnorm_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: img_cmrnorm_layer
-    :noindex:
-
-batch_norm_layer
----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: batch_norm_layer
-    :noindex:
-
-sum_to_one_norm_layer
----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sum_to_one_norm_layer
-    :noindex:
-    
-Recurrent Layers
-================
-
-recurrent_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: recurrent_layer
-    :noindex:
-
-lstmemory
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: lstmemory
-    :noindex:
-
-grumemory
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: grumemory
-    :noindex:
-
-Recurrent Layer Group
-=====================
-
-memory
-------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: memory
-    :noindex:
-
-recurrent_group
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: recurrent_group
-    :noindex:
-    
-lstm_step_layer
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: lstm_step_layer
-    :noindex:
-
-gru_step_layer
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: gru_step_layer
-    :noindex:
-
-beam_search
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: beam_search
-    :noindex:
-    
-get_output_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: get_output_layer
-    :noindex:
-    
-Mixed Layer
-===========
-
-..  _api_trainer_config_helpers_layers_mixed_layer:
-
-mixed_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: mixed_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_embedding_layer:
-
-embedding_layer
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: embedding_layer
-    :noindex:
-
-scaling_projection
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: scaling_projection
-    :noindex:
-
-dotmul_projection
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: dotmul_projection
-    :noindex:
-
-dotmul_operator
----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: dotmul_operator
-    :noindex:
-
-full_matrix_projection
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: full_matrix_projection
-    :noindex:
-
-identity_projection
--------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: identity_projection
-    :noindex:
-
-
-table_projection
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: table_projection
-    :noindex:
-
-trans_full_matrix_projection
-----------------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: trans_full_matrix_projection
-    :noindex:
-    
-Aggregate Layers
-================
-
-..  _api_trainer_config_helpers_layers_pooling_layer:
-
-pooling_layer
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: pooling_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_last_seq:
-
-last_seq
---------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: last_seq
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_first_seq:
-
-first_seq
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: first_seq
-    :noindex:
-
-concat_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: concat_layer
-    :noindex:
-
-seq_concat_layer
-----------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: seq_concat_layer
-    :noindex:
-
-Reshaping Layers
-================
-
-block_expand_layer
-------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: block_expand_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_expand_layer:
-
-expand_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: expand_layer
-    :noindex:
-
-repeat_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: repeat_layer
-    :noindex:
-
-rotate_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: rotate_layer
-    :noindex:
-
-seq_reshape_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: seq_reshape_layer
-    :noindex:
-
-Math Layers
-===========
-
-addto_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: addto_layer
-    :noindex:
-
-linear_comb_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: linear_comb_layer
-    :noindex:
-
-interpolation_layer
--------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: interpolation_layer
-    :noindex:
-
-bilinear_interp_layer
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: bilinear_interp_layer
-    :noindex:
-
-power_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: power_layer
-    :noindex:
-
-scaling_layer
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: scaling_layer
-    :noindex:
-
-slope_intercept_layer
-----------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: slope_intercept_layer
-    :noindex:
-
-tensor_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: tensor_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_cos_sim:
-
-cos_sim
--------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: cos_sim
-    :noindex:
-
-trans_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: trans_layer
-    :noindex:
-
-Sampling Layers
-===============
-
-maxid_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: maxid_layer
-    :noindex:
-
-sampling_id_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sampling_id_layer
-    :noindex:
-
-Slicing and Joining Layers
-==========================
-
-pad_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: pad_layer
-    :noindex:
-
-..  _api_trainer_config_helpers_layers_cost_layers:
-
-Cost Layers
-===========
-
-cross_entropy
--------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: cross_entropy
-    :noindex:
-
-cross_entropy_with_selfnorm
----------------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: cross_entropy_with_selfnorm
-    :noindex:
-
-multi_binary_label_cross_entropy
---------------------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :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
-    :members: huber_cost
-    :noindex:
-
-lambda_cost
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: lambda_cost
-    :noindex:
-
-rank_cost
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: rank_cost
-    :noindex:
-
-sum_cost
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: sum_cost
-    :noindex:
-
-crf_layer
------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: crf_layer
-    :noindex:
-
-crf_decoding_layer
--------------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: crf_decoding_layer
-    :noindex:
-
-ctc_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: ctc_layer
-    :noindex:
-
-warp_ctc_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: warp_ctc_layer
-    :noindex:
-
-nce_layer
------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: nce_layer
-    :noindex:
-
-hsigmoid
----------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: hsigmoid
-    :noindex:
-
-smooth_l1_cost
---------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: smooth_l1_cost
-    :noindex:
-
-Check Layer 
-============
-
-eos_layer
-------------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: eos_layer
-    :noindex:

develop/doc_cn/_sources/api/v1/trainer_config_helpers/networks.rst.txt

-========
-Networks
-========
-
-The networks module contains pieces of neural network that combine multiple layers.
-
-NLP
-===
-
-sequence_conv_pool
-------------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: sequence_conv_pool
-    :noindex:
-
-..  _api_trainer_config_helpers_network_text_conv_pool:
-
-text_conv_pool
---------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: text_conv_pool
-    :noindex:
-
-Images
-======
-
-img_conv_bn_pool
-----------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: img_conv_bn_pool
-    :noindex:
-
-img_conv_group
---------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: img_conv_group
-    :noindex:
-
-..  _api_trainer_config_helpers_network_simple_img_conv_pool:
-
-simple_img_conv_pool
---------------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_img_conv_pool
-    :noindex:
-
-vgg_16_network
----------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: vgg_16_network
-    :noindex:
-
-Recurrent
-=========
-
-LSTM
-----
-
-lstmemory_unit
-``````````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: lstmemory_unit
-    :noindex:
-
-lstmemory_group
-```````````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: lstmemory_group
-    :noindex:
-
-simple_lstm
-```````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_lstm
-    :noindex:
-
-bidirectional_lstm
-``````````````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: bidirectional_lstm
-    :noindex:
-
-GRU
----
-
-gru_unit
-````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: gru_unit
-    :noindex:
-
-gru_group
-`````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: gru_group
-    :noindex:
-
-simple_gru
-``````````
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_gru
-    :noindex:
-
-simple_attention
-----------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: simple_attention
-    :noindex:
-
-Miscs
-=====
-
-dropout_layer
---------------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: dropout_layer
-    :noindex:
-
-outputs
--------
-..  automodule:: paddle.trainer_config_helpers.networks
-    :members: outputs
-    :noindex:

develop/doc_cn/_sources/api/v1/trainer_config_helpers/optimizers.rst.txt

-..  _api_trainer_config_helpers_optimizers:
-
-==========
-Optimizers
-==========
-
-BaseSGDOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: BaseSGDOptimizer
-    :noindex:
-
-MomentumOptimizer
-=================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: MomentumOptimizer
-    :noindex:
-
-AdamOptimizer
-=============
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdamOptimizer
-    :noindex:
-
-AdamaxOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdamaxOptimizer
-    :noindex:
-
-AdaGradOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdaGradOptimizer
-    :noindex:
-
-DecayedAdaGradOptimizer
-=======================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: DecayedAdaGradOptimizer
-    :noindex:
-
-AdaDeltaOptimizer
-=================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: AdaDeltaOptimizer
-    :noindex:
-
-RMSPropOptimizer
-================
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: RMSPropOptimizer
-    :noindex:
-
-..  _api_trainer_config_helpers_optimizers_settings:
-
-settings
-========
-..  automodule:: paddle.trainer_config_helpers.optimizers
-    :members: settings
-    :noindex:

develop/doc_cn/_sources/api/v1/trainer_config_helpers/poolings.rst.txt

-========
-Poolings
-========
-
-BasePoolingType
-===============
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: BasePoolingType
-    :noindex:
-
-AvgPooling
-==========
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: AvgPooling
-    :noindex:
-
-MaxPooling
-==========
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: MaxPooling
-    :noindex:
-
-SumPooling
-==========
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: SumPooling
-    :noindex:
-
-SquareRootNPooling
-==================
-..  automodule:: paddle.trainer_config_helpers.poolings
-    :members: SquareRootNPooling
-    :noindex:

develop/doc_cn/_sources/api/v2/config/evaluators.rst.txt

+..  _api_v2:
+
+==========
+Evaluators
+==========
+
+Classification 
+==============
+
+classification_error
+--------------------
+..  automodule:: paddle.v2.evaluator
+    :members: classification_error
+    :noindex:
+
+auc
+---
+..  automodule:: paddle.v2.evaluator
+    :members: auc
+    :noindex:
+
+ctc_error
+---------
+..  automodule:: paddle.v2.evaluator
+    :members: ctc_error
+    :noindex:
+
+chunk
+-----
+..  automodule:: paddle.v2.evaluator
+    :members: chunk
+    :noindex:
+
+precision_recall
+----------------
+..  automodule:: paddle.v2.evaluator
+    :members:  precision_recall
+    :noindex:
+
+Rank
+====
+
+pnpair
+------
+..  automodule:: paddle.v2.evaluator
+    :members:  pnpair
+    :noindex:
+
+Utils
+=====
+
+sum
+---
+..  automodule:: paddle.v2.evaluator
+    :members: sum
+    :noindex:
+
+column_sum
+----------
+..  automodule:: paddle.v2.evaluator
+    :members: column_sum
+    :noindex:
+
+Print
+=====
+
+classification_error_printer
+----------------------------
+..  automodule:: paddle.v2.evaluator
+    :members:  classification_error_printer
+    :noindex:
+
+gradient_printer
+----------------
+..  automodule:: paddle.v2.evaluator
+    :members:  gradient_printer
+    :noindex:
+
+maxid_printer
+-------------
+..  automodule:: paddle.v2.evaluator
+    :members:  maxid_printer
+    :noindex:
+
+maxframe_printer
+----------------
+..  automodule:: paddle.v2.evaluator
+    :members:  maxframe_printer
+    :noindex:
+
+seqtext_printer
+---------------
+..  automodule:: paddle.v2.evaluator
+    :members:  seqtext_printer
+    :noindex:
+
+value_printer
+-------------
+..  automodule:: paddle.v2.evaluator
+    :members:  value_printer
+    :noindex:

develop/doc_cn/_sources/api/v2/config/networks.rst.txt

@@ -44,6 +44,12 @@ simple_img_conv_pool
     :members: simple_img_conv_pool
     :noindex:
 
+small_vgg
+---------
+..  automodule:: paddle.v2.networks
+    :members: small_vgg
+    :noindex:
+
 vgg_16_network
 ---------------
 ..  automodule:: paddle.v2.networks
@@ -101,6 +107,18 @@ simple_gru
     :members: simple_gru
     :noindex:
 
+simple_gru2
+```````````
+..  automodule:: paddle.v2.networks
+    :members: simple_gru2
+    :noindex:
+
+bidirectional_gru
+``````````````````
+..  automodule:: paddle.v2.networks
+    :members: bidirectional_gru
+    :noindex:
+
 simple_attention
 ----------------
 ..  automodule:: paddle.v2.networks

develop/doc_cn/_sources/api/v2/model_configs.rst.txt

@@ -6,6 +6,7 @@ Model Configuration
 
     config/activation.rst
     config/layer.rst
+    config/evaluators.rst
     config/optimizer.rst
     config/pooling.rst
     config/networks.rst

develop/doc_cn/about/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/api/index_cn.html

@@ -144,6 +144,7 @@
 <li class="toctree-l2"><a class="reference internal" href="v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="v2/config/networks.html">Networks</a></li>

develop/doc_cn/api/v1/data_provider/dataprovider_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>

develop/doc_cn/api/v1/data_provider/pydataprovider2_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>

develop/doc_cn/api/v1/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../v2/config/networks.html">Networks</a></li>
@@ -196,16 +197,6 @@
 <div class="section" id="model-config-api">
 <span id="api-trainer-config"></span><h2>Model Config API<a class="headerlink" href="#model-config-api" title="永久链接至标题">¶</a></h2>
 <div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/optimizers.html">Optimizers</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/data_sources.html">DataSources</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/layers.html">Layers</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/activations.html">Activations</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/poolings.html">Poolings</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/networks.html">Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/evaluators.html">Evaluators</a></li>
-<li class="toctree-l1"><a class="reference internal" href="trainer_config_helpers/attrs.html">Parameter Attributes</a></li>
-</ul>
 </div>
 </div>
 <div class="section" id="applications-api">

develop/doc_cn/api/v1/predict/swig_py_paddle_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>

develop/doc_cn/api/v1/trainer_config_helpers/activations.html

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-    <li>Activations</li>
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-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-            
-  <div class="section" id="activations">
-<h1>Activations<a class="headerlink" href="#activations" title="永久链接至标题">¶</a></h1>
-<div class="section" id="baseactivation">
-<h2>BaseActivation<a class="headerlink" href="#baseactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">BaseActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Base</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="absactivation">
-<h2>AbsActivation<a class="headerlink" href="#absactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">AbsActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Abs</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="expactivation">
-<h2>ExpActivation<a class="headerlink" href="#expactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">ExpActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Exp</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="identityactivation">
-<h2>IdentityActivation<a class="headerlink" href="#identityactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">IdentityActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Linear</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="linearactivation">
-<h2>LinearActivation<a class="headerlink" href="#linearactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">LinearActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Linear</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="logactivation">
-<h2>LogActivation<a class="headerlink" href="#logactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">LogActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Log</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="squareactivation">
-<h2>SquareActivation<a class="headerlink" href="#squareactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SquareActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Square</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="sigmoidactivation">
-<h2>SigmoidActivation<a class="headerlink" href="#sigmoidactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SigmoidActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Sigmoid</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="softmaxactivation">
-<h2>SoftmaxActivation<a class="headerlink" href="#softmaxactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SoftmaxActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Softmax</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="sequencesoftmaxactivation">
-<h2>SequenceSoftmaxActivation<a class="headerlink" href="#sequencesoftmaxactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SequenceSoftmaxActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">SequenceSoftmax</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="reluactivation">
-<h2>ReluActivation<a class="headerlink" href="#reluactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">ReluActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Relu</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="breluactivation">
-<h2>BReluActivation<a class="headerlink" href="#breluactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">BReluActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">BRelu</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="softreluactivation">
-<h2>SoftReluActivation<a class="headerlink" href="#softreluactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">SoftReluActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">SoftRelu</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="tanhactivation">
-<h2>TanhActivation<a class="headerlink" href="#tanhactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">TanhActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Tanh</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="stanhactivation">
-<h2>STanhActivation<a class="headerlink" href="#stanhactivation" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.activations.</code><code class="descname">STanhActivation</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">STanh</span></code> 的别名</p>
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-  <div class="section" id="module-paddle.trainer_config_helpers.attrs">
-<span id="parameter-attributes"></span><h1>Parameter Attributes<a class="headerlink" href="#module-paddle.trainer_config_helpers.attrs" title="永久链接至标题">¶</a></h1>
-<dl class="class">
-<dt id="paddle.trainer_config_helpers.attrs.ParameterAttribute">
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ParameterAttribute</code><span class="sig-paren">(</span><em>name=None</em>, <em>is_static=False</em>, <em>initial_std=None</em>, <em>initial_mean=None</em>, <em>initial_max=None</em>, <em>initial_min=None</em>, <em>l1_rate=None</em>, <em>l2_rate=None</em>, <em>learning_rate=None</em>, <em>momentum=None</em>, <em>gradient_clipping_threshold=None</em>, <em>sparse_update=False</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="永久链接至目标">¶</a></dt>
-<dd><p>Parameter Attributes object. To fine-tuning network training process, user
-can set attribute to control training details, such as l1,l2 rate / learning
-rate / how to init param.</p>
-<p>NOTE: IT IS A HIGH LEVEL USER INTERFACE.</p>
-<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 last simple">
-<li><strong>is_static</strong> (<em>bool</em>) &#8211; True if this parameter will be fixed while training.</li>
-<li><strong>initial_std</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Gauss Random initialization standard deviation.
-None if not using Gauss Random initialize parameter.</li>
-<li><strong>initial_mean</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Gauss Random initialization mean.
-None if not using Gauss Random initialize parameter.</li>
-<li><strong>initial_max</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Uniform initialization max value.</li>
-<li><strong>initial_min</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; Uniform initialization min value.</li>
-<li><strong>l1_rate</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; the l1 regularization factor</li>
-<li><strong>l2_rate</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; the l2 regularization factor</li>
-<li><strong>learning_rate</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; The parameter learning rate. None means 1.
-The learning rate when optimize is LEARNING_RATE =
-GLOBAL_LEARNING_RATE * PARAMETER_LEARNING_RATE
-* SCHEDULER_FACTOR.</li>
-<li><strong>momentum</strong> (<em>float</em><em> or </em><em>None</em>) &#8211; The parameter momentum. None means use global value.</li>
-<li><strong>gradient_clipping_threshold</strong> (<em>float</em>) &#8211; gradient clipping threshold. If gradient
-value larger than some value, will be
-clipped.</li>
-<li><strong>sparse_update</strong> (<em>bool</em>) &#8211; Enable sparse update for this parameter. It will
-enable both local and remote sparse update.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt id="paddle.trainer_config_helpers.attrs.ParameterAttribute.set_default_parameter_name">
-<code class="descname">set_default_parameter_name</code><span class="sig-paren">(</span><em>name</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ParameterAttribute.set_default_parameter_name" title="永久链接至目标">¶</a></dt>
-<dd><p>Set default parameter name. If parameter not set, then will use default
-parameter name.</p>
-<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"><strong>name</strong> (<em>basestring</em>) &#8211; default parameter name.</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute">
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ExtraLayerAttribute</code><span class="sig-paren">(</span><em>error_clipping_threshold=None</em>, <em>drop_rate=None</em>, <em>device=None</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="永久链接至目标">¶</a></dt>
-<dd><p>Some high level layer attributes config. You can set all attributes here,
-but some layer doesn&#8217;t support all attributes. If you set an attribute to a
-layer that not support this attribute, paddle will print an error and core.</p>
-<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 last simple">
-<li><strong>error_clipping_threshold</strong> (<em>float</em>) &#8211; Error clipping threshold.</li>
-<li><strong>drop_rate</strong> (<em>float</em>) &#8211; Dropout rate. Dropout will create a mask on layer output.
-The dropout rate is the zero rate of this mask. The
-details of what dropout is please refer to <a class="reference external" href="https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf">here</a>.</li>
-<li><strong>device</strong> (<em>int</em>) &#8211; <p>device ID of layer. device=-1, use CPU. device&gt;=0, use GPU.
-The details allocation in parallel_nn please refer to <a class="reference external" href="http://www.paddlepaddle.org/doc/ui/cmd_argument/use_case.html#case-2-specify-layers-in-different-devices">here</a>.</p>
-</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="paddle.trainer_config_helpers.attrs.ParamAttr">
-<code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ParamAttr</code><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ParamAttr" title="永久链接至目标">¶</a></dt>
-<dd><p><a class="reference internal" href="#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><code class="xref py py-class docutils literal"><span class="pre">ParameterAttribute</span></code></a> 的别名</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="paddle.trainer_config_helpers.attrs.ExtraAttr">
-<code class="descclassname">paddle.trainer_config_helpers.attrs.</code><code class="descname">ExtraAttr</code><a class="headerlink" href="#paddle.trainer_config_helpers.attrs.ExtraAttr" title="永久链接至目标">¶</a></dt>
-<dd><p><a class="reference internal" href="#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><code class="xref py py-class docutils literal"><span class="pre">ExtraLayerAttribute</span></code></a> 的别名</p>
-</dd></dl>
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-  <div class="section" id="module-paddle.trainer_config_helpers.data_sources">
-<span id="datasources"></span><span id="api-trainer-config-helpers-data-sources"></span><h1>DataSources<a class="headerlink" href="#module-paddle.trainer_config_helpers.data_sources" title="永久链接至标题">¶</a></h1>
-<p>Data Sources are helpers to define paddle training data or testing data.</p>
-<dl class="function">
-<dt id="paddle.trainer_config_helpers.data_sources.define_py_data_sources2">
-<code class="descclassname">paddle.trainer_config_helpers.data_sources.</code><code class="descname">define_py_data_sources2</code><span class="sig-paren">(</span><em>train_list</em>, <em>test_list</em>, <em>module</em>, <em>obj</em>, <em>args=None</em><span class="sig-paren">)</span><a class="headerlink" href="#paddle.trainer_config_helpers.data_sources.define_py_data_sources2" title="永久链接至目标">¶</a></dt>
-<dd><p>Define python Train/Test data sources in one method. If train/test use
-the same Data Provider configuration, module/obj/args contain one argument,
-otherwise contain a list or tuple of arguments. For example:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">define_py_data_sources2</span><span class="p">(</span><span class="n">train_list</span><span class="o">=</span><span class="s2">&quot;train.list&quot;</span><span class="p">,</span>
-                        <span class="n">test_list</span><span class="o">=</span><span class="s2">&quot;test.list&quot;</span><span class="p">,</span>
-                        <span class="n">module</span><span class="o">=</span><span class="s2">&quot;data_provider&quot;</span>
-                        <span class="c1"># if train/test use different configurations,</span>
-                        <span class="c1"># obj=[&quot;process_train&quot;, &quot;process_test&quot;]</span>
-                        <span class="n">obj</span><span class="o">=</span><span class="s2">&quot;process&quot;</span><span class="p">,</span>
-                        <span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;dictionary&quot;</span><span class="p">:</span> <span class="n">dict_name</span><span class="p">})</span>
-</pre></div>
-</div>
-<p>The related data provider can refer to <span class="xref std std-ref">api_pydataprovider2_sequential_model</span> .</p>
-<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>train_list</strong> (<em>basestring</em>) &#8211; Train list name.</li>
-<li><strong>test_list</strong> (<em>basestring</em>) &#8211; Test list name.</li>
-<li><strong>module</strong> (<em>basestring</em><em> or </em><em>tuple</em><em> or </em><em>list</em>) &#8211; python module name. If train and test is different, then
-pass a tuple or list to this argument.</li>
-<li><strong>obj</strong> (<em>basestring</em><em> or </em><em>tuple</em><em> or </em><em>list</em>) &#8211; python object name. May be a function name if using
-PyDataProviderWrapper. If train and test is different, then pass
-a tuple or list to this argument.</li>
-<li><strong>args</strong> (<em>string</em><em> or </em><em>picklable object</em><em> or </em><em>list</em><em> or </em><em>tuple.</em>) &#8211; The best practice is using dict() to pass arguments into
-DataProvider, and use <code class="code docutils literal"><span class="pre">&#64;init_hook_wrapper</span></code> to receive
-arguments. If train and test is different, then pass a tuple
-or list to this argument.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">None</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">None</p>
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-  <ul class="wy-breadcrumbs">
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-    <li>Networks</li>
-  </ul>
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-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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-            
-  <div class="section" id="networks">
-<h1>Networks<a class="headerlink" href="#networks" title="永久链接至标题">¶</a></h1>
-<p>The networks module contains pieces of neural network that combine multiple layers.</p>
-<div class="section" id="nlp">
-<h2>NLP<a class="headerlink" href="#nlp" title="永久链接至标题">¶</a></h2>
-<div class="section" id="sequence-conv-pool">
-<h3>sequence_conv_pool<a class="headerlink" href="#sequence-conv-pool" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">sequence_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Text convolution pooling layers helper.</p>
-<p>Text input =&gt; Context Projection =&gt; FC Layer =&gt; Pooling =&gt; Output.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; name of output layer(pooling layer name)</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; name of input layer</li>
-<li><strong>context_len</strong> (<em>int</em>) &#8211; context projection length. See
-context_projection&#8217;s document.</li>
-<li><strong>hidden_size</strong> (<em>int</em>) &#8211; FC Layer size.</li>
-<li><strong>context_start</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; context projection length. See
-context_projection&#8217;s context_start.</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType.</em>) &#8211; pooling layer type. See pooling_layer&#8217;s document.</li>
-<li><strong>context_proj_layer_name</strong> (<em>basestring</em>) &#8211; context projection layer name.
-None if user don&#8217;t care.</li>
-<li><strong>context_proj_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; context projection parameter attribute.
-None if user don&#8217;t care.</li>
-<li><strong>fc_layer_name</strong> (<em>basestring</em>) &#8211; fc layer name. None if user don&#8217;t care.</li>
-<li><strong>fc_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
-<li><strong>fc_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc bias parameter attribute. False if no bias,
-None if user don&#8217;t care.</li>
-<li><strong>fc_act</strong> (<em>BaseActivation</em>) &#8211; fc layer activation type. None means tanh</li>
-<li><strong>pool_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
-False if no bias.</li>
-<li><strong>fc_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; fc layer extra attribute.</li>
-<li><strong>context_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; context projection layer extra attribute.</li>
-<li><strong>pool_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; pooling layer extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">output layer name.</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="text-conv-pool">
-<span id="api-trainer-config-helpers-network-text-conv-pool"></span><h3>text_conv_pool<a class="headerlink" href="#text-conv-pool" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">text_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Text convolution pooling layers helper.</p>
-<p>Text input =&gt; Context Projection =&gt; FC Layer =&gt; Pooling =&gt; Output.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; name of output layer(pooling layer name)</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; name of input layer</li>
-<li><strong>context_len</strong> (<em>int</em>) &#8211; context projection length. See
-context_projection&#8217;s document.</li>
-<li><strong>hidden_size</strong> (<em>int</em>) &#8211; FC Layer size.</li>
-<li><strong>context_start</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; context projection length. See
-context_projection&#8217;s context_start.</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType.</em>) &#8211; pooling layer type. See pooling_layer&#8217;s document.</li>
-<li><strong>context_proj_layer_name</strong> (<em>basestring</em>) &#8211; context projection layer name.
-None if user don&#8217;t care.</li>
-<li><strong>context_proj_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; context projection parameter attribute.
-None if user don&#8217;t care.</li>
-<li><strong>fc_layer_name</strong> (<em>basestring</em>) &#8211; fc layer name. None if user don&#8217;t care.</li>
-<li><strong>fc_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
-<li><strong>fc_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em>) &#8211; fc bias parameter attribute. False if no bias,
-None if user don&#8217;t care.</li>
-<li><strong>fc_act</strong> (<em>BaseActivation</em>) &#8211; fc layer activation type. None means tanh</li>
-<li><strong>pool_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
-False if no bias.</li>
-<li><strong>fc_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; fc layer extra attribute.</li>
-<li><strong>context_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; context projection layer extra attribute.</li>
-<li><strong>pool_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; pooling layer extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">output layer name.</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="images">
-<h2>Images<a class="headerlink" href="#images" title="永久链接至标题">¶</a></h2>
-<div class="section" id="img-conv-bn-pool">
-<h3>img_conv_bn_pool<a class="headerlink" href="#img-conv-bn-pool" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">img_conv_bn_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Convolution, batch normalization, pooling group.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; group name</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; layer&#8217;s input</li>
-<li><strong>filter_size</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
-<li><strong>num_filters</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
-<li><strong>pool_size</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; see batch_norm_layer&#8217;s document.</li>
-<li><strong>groups</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
-<li><strong>conv_stride</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_padding</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>num_channel</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>shared_bias</strong> (<em>bool</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>conv_layer_attr</strong> (<em>ExtraLayerOutput</em>) &#8211; see img_conv_layer&#8217;s document.</li>
-<li><strong>bn_param_attr</strong> (<em>ParameterAttribute.</em>) &#8211; see batch_norm_layer&#8217;s document.</li>
-<li><strong>bn_bias_attr</strong> &#8211; see batch_norm_layer&#8217;s document.</li>
-<li><strong>bn_layer_attr</strong> &#8211; ParameterAttribute.</li>
-<li><strong>pool_stride</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>pool_padding</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
-<li><strong>pool_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; see img_pool_layer&#8217;s document.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Layer groups output</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="img-conv-group">
-<h3>img_conv_group<a class="headerlink" href="#img-conv-group" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">img_conv_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Image Convolution Group, Used for vgg net.</p>
-<p>TODO(yuyang18): Complete docs</p>
-<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>conv_batchnorm_drop_rate</strong> &#8211; </li>
-<li><strong>input</strong> &#8211; </li>
-<li><strong>conv_num_filter</strong> &#8211; </li>
-<li><strong>pool_size</strong> &#8211; </li>
-<li><strong>num_channels</strong> &#8211; </li>
-<li><strong>conv_padding</strong> &#8211; </li>
-<li><strong>conv_filter_size</strong> &#8211; </li>
-<li><strong>conv_act</strong> &#8211; </li>
-<li><strong>conv_with_batchnorm</strong> &#8211; </li>
-<li><strong>pool_stride</strong> &#8211; </li>
-<li><strong>pool_type</strong> &#8211; </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="simple-img-conv-pool">
-<span id="api-trainer-config-helpers-network-simple-img-conv-pool"></span><h3>simple_img_conv_pool<a class="headerlink" href="#simple-img-conv-pool" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_img_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Simple image convolution and pooling group.</p>
-<p>Input =&gt; conv =&gt; pooling</p>
-<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>name</strong> (<em>basestring</em>) &#8211; group name</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
-<li><strong>filter_size</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>num_filters</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>pool_size</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>groups</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>conv_stride</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>conv_padding</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>bias_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer for details</li>
-<li><strong>num_channel</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; see img_conv_layer for details</li>
-<li><strong>shared_bias</strong> (<em>bool</em>) &#8211; see img_conv_layer for details</li>
-<li><strong>conv_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; see img_conv_layer for details</li>
-<li><strong>pool_stride</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>pool_padding</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
-<li><strong>pool_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; see img_pool_layer for details</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Layer&#8217;s output</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="vgg-16-network">
-<h3>vgg_16_network<a class="headerlink" href="#vgg-16-network" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">vgg_16_network</code><span class="sig-paren">(</span><em>input_image</em>, <em>num_channels</em>, <em>num_classes=1000</em><span class="sig-paren">)</span></dt>
-<dd><p>Same model from <a class="reference external" href="https://gist.github.com/ksimonyan/211839e770f7b538e2d8">https://gist.github.com/ksimonyan/211839e770f7b538e2d8</a></p>
-<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>num_classes</strong> &#8211; </li>
-<li><strong>input_image</strong> (<em>LayerOutput</em>) &#8211; </li>
-<li><strong>num_channels</strong> (<em>int</em>) &#8211; </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-</div>
-<div class="section" id="recurrent">
-<h2>Recurrent<a class="headerlink" href="#recurrent" title="永久链接至标题">¶</a></h2>
-<div class="section" id="lstm">
-<h3>LSTM<a class="headerlink" href="#lstm" title="永久链接至标题">¶</a></h3>
-<div class="section" id="lstmemory-unit">
-<h4>lstmemory_unit<a class="headerlink" href="#lstmemory-unit" title="永久链接至标题">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">lstmemory_unit</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Define calculations that a LSTM unit performs in a single time step.
-This function itself is not a recurrent layer, so that it can not be
-directly applied to sequence input. This function is always used in
-recurrent_group (see layers.py for more details) to implement attention
-mechanism.</p>
-<p>Please refer to  <strong>Generating Sequences With Recurrent Neural Networks</strong>
-for more details about LSTM. The link goes as follows:
-.. _Link: <a class="reference external" href="https://arxiv.org/abs/1308.0850">https://arxiv.org/abs/1308.0850</a></p>
-<div class="math">
-\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">lstm_step</span> <span class="o">=</span> <span class="n">lstmemory_unit</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span>
-                           <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
-                           <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                           <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
-                           <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; lstmemory unit name.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstmemory unit size.</li>
-<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
-<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
-<li><strong>mixed_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of mixed layer.
-False means no bias, None means default bias.</li>
-<li><strong>lstm_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of lstm layer.
-False means no bias, None means default bias.</li>
-<li><strong>mixed_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; mixed layer&#8217;s extra attribute.</li>
-<li><strong>lstm_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; lstm layer&#8217;s extra attribute.</li>
-<li><strong>get_output_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; get output layer&#8217;s extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">lstmemory unit name.</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="lstmemory-group">
-<h4>lstmemory_group<a class="headerlink" href="#lstmemory-group" title="永久链接至标题">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">lstmemory_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>lstm_group is a recurrent layer group version of Long Short Term Memory. It
-does exactly the same calculation as the lstmemory layer (see lstmemory in
-layers.py for the maths) does. A promising benefit is that LSTM memory
-cell states, or hidden states in every time step are accessible to the
-user. This is especially useful in attention model. If you do not need to
-access the internal states of the lstm, but merely use its outputs,
-it is recommended to use the lstmemory, which is relatively faster than
-lstmemory_group.</p>
-<p>NOTE: In PaddlePaddle&#8217;s implementation, the following input-to-hidden
-multiplications:
-<span class="math">\(W_{xi}x_{t}\)</span> , <span class="math">\(W_{xf}x_{t}\)</span>,
-<span class="math">\(W_{xc}x_t\)</span>, <span class="math">\(W_{xo}x_{t}\)</span> are not done in lstmemory_unit to
-speed up the calculations. Consequently, an additional mixed_layer with
-full_matrix_projection must be included before lstmemory_unit is called.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">lstm_step</span> <span class="o">=</span> <span class="n">lstmemory_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span>
-                            <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
-                            <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                            <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
-                            <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; lstmemory group name.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstmemory group size.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; is lstm reversed</li>
-<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
-<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
-<li><strong>mixed_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of mixed layer.
-False means no bias, None means default bias.</li>
-<li><strong>lstm_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of lstm layer.
-False means no bias, None means default bias.</li>
-<li><strong>mixed_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; mixed layer&#8217;s extra attribute.</li>
-<li><strong>lstm_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; lstm layer&#8217;s extra attribute.</li>
-<li><strong>get_output_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; get output layer&#8217;s extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the lstmemory group.</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="simple-lstm">
-<h4>simple_lstm<a class="headerlink" href="#simple-lstm" title="永久链接至标题">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_lstm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Simple LSTM Cell.</p>
-<p>It just combine a mixed layer with fully_matrix_projection and a lstmemory
-layer. The simple lstm cell was implemented as follow equations.</p>
-<div class="math">
-\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
-<p>Please refer <strong>Generating Sequences With Recurrent Neural Networks</strong> if you
-want to know what lstm is. <a class="reference external" href="http://arxiv.org/abs/1308.0850">Link</a> is here.</p>
-<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>name</strong> (<em>basestring</em>) &#8211; lstm layer name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstm layer size.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
-<li><strong>mat_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; mixed layer&#8217;s matrix projection parameter attribute.</li>
-<li><strong>bias_param_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute. False means no bias, None
-means default bias.</li>
-<li><strong>inner_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; lstm cell parameter attribute.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
-<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
-<li><strong>mixed_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; mixed layer&#8217;s extra attribute.</li>
-<li><strong>lstm_cell_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; lstm layer&#8217;s extra attribute.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">lstm layer name.</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="bidirectional-lstm">
-<h4>bidirectional_lstm<a class="headerlink" href="#bidirectional-lstm" title="永久链接至标题">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">bidirectional_lstm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>A bidirectional_lstm is a recurrent unit that iterates over the input
-sequence both in forward and bardward orders, and then concatenate two
-outputs form a final output. However, concatenation of two outputs
-is not the only way to form the final output, you can also, for example,
-just add them together.</p>
-<p>Please refer to  <strong>Neural Machine Translation by Jointly Learning to Align
-and Translate</strong> for more details about the bidirectional lstm.
-The link goes as follows:
-.. _Link: <a class="reference external" href="https://arxiv.org/pdf/1409.0473v3.pdf">https://arxiv.org/pdf/1409.0473v3.pdf</a></p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">bi_lstm</span> <span class="o">=</span> <span class="n">bidirectional_lstm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</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>name</strong> (<em>basestring</em>) &#8211; bidirectional lstm layer name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; lstm layer size.</li>
-<li><strong>return_seq</strong> (<em>bool</em>) &#8211; If set False, outputs of the last time step are
-concatenated and returned.
-If set True, the entire output sequences that are
-processed in forward and backward directions are
-concatenated and returned.</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 accroding to the return_seq.</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="gru">
-<h3>GRU<a class="headerlink" href="#gru" title="永久链接至标题">¶</a></h3>
-<div class="section" id="gru-unit">
-<h4>gru_unit<a class="headerlink" href="#gru-unit" title="永久链接至标题">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">gru_unit</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Define calculations that a gated recurrent unit performs in a single time
-step. This function itself is not a recurrent layer, so that it can not be
-directly applied to sequence input. This function is almost always used in
-the recurrent_group (see layers.py for more details) to implement attention
-mechanism.</p>
-<p>Please see grumemory in layers.py for the details about the maths.</p>
-<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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activation</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activation</li>
-<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru output layer.</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="gru-group">
-<h4>gru_group<a class="headerlink" href="#gru-group" title="永久链接至标题">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">gru_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>gru_group is a recurrent layer group version of Gated Recurrent Unit. It
-does exactly the same calculation as the grumemory layer does. A promising
-benefit is that gru hidden states are accessible to the user. This is
-especially useful in attention model. If you do not need to access
-any internal state, but merely use the outputs of a GRU, it is recommended
-to use the grumemory, which is relatively faster.</p>
-<p>Please see grumemory in layers.py for more detail about the maths.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">gur_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span>
-                <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
-                <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
-                <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activiation</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activiation</li>
-<li><strong>gru_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
-<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru group.</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="simple-gru">
-<h4>simple_gru<a class="headerlink" href="#simple-gru" title="永久链接至标题">¶</a></h4>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_gru</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group,
-simple_gru in network.py. The reason why there are so many interfaces is
-that we have two ways to implement recurrent neural network. One way is to
-use one complete layer to implement rnn (including simple rnn, gru and lstm)
-with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But,
-the multiplication operation <span class="math">\(W x_t\)</span> is not computed in these layers.
-See details in their interfaces in layers.py.
-The other implementation is to use an recurrent group which can ensemble a
-series of layers to compute rnn step by step. This way is flexible for
-attenion mechanism or other complex connections.</p>
-<ul class="simple">
-<li>gru_step_layer: only compute rnn by one step. It needs an memory as input
-and can be used in recurrent group.</li>
-<li>gru_unit: a wrapper of gru_step_layer with memory.</li>
-<li>gru_group: a GRU cell implemented by a combination of multiple layers in
-recurrent group.
-But <span class="math">\(W x_t\)</span> is not done in group.</li>
-<li>gru_memory: a GRU cell implemented by one layer, which does same calculation
-with gru_group and is faster than gru_group.</li>
-<li>simple_gru: a complete GRU implementation inlcuding <span class="math">\(W x_t\)</span> and
-gru_group. <span class="math">\(W\)</span> contains <span class="math">\(W_r\)</span>, <span class="math">\(W_z\)</span> and <span class="math">\(W\)</span>, see
-formula in grumemory.</li>
-</ul>
-<p>The computational speed is that, grumemory is relatively better than
-gru_group, and gru_group is relatively better than simple_gru.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">simple_gru</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</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; input layer name.</li>
-<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
-<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
-<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
-<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activiation</li>
-<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activiation</li>
-<li><strong>gru_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
-<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru group.</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="simple-attention">
-<h3>simple_attention<a class="headerlink" href="#simple-attention" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">simple_attention</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Calculate and then return a context vector by attention machanism.
-Size of the context vector equals to size of the encoded_sequence.</p>
-<div class="math">
-\[ \begin{align}\begin{aligned}a(s_{i-1},h_{j}) &amp; = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})\\e_{i,j} &amp; = a(s_{i-1}, h_{j})\\a_{i,j} &amp; = \frac{exp(e_{i,j})}{\sum_{k=1}^{T_x}{exp(e_{i,k})}}\\c_{i} &amp; = \sum_{j=1}^{T_{x}}a_{i,j}h_{j}\end{aligned}\end{align} \]</div>
-<p>where <span class="math">\(h_{j}\)</span> is the jth element of encoded_sequence,
-<span class="math">\(U_{a}h_{j}\)</span> is the jth element of encoded_proj
-<span class="math">\(s_{i-1}\)</span> is decoder_state
-<span class="math">\(f\)</span> is weight_act, and is set to tanh by default.</p>
-<p>Please refer to <strong>Neural Machine Translation by Jointly Learning to
-Align and Translate</strong> for more details. The link is as follows:
-<a class="reference external" href="https://arxiv.org/abs/1409.0473">https://arxiv.org/abs/1409.0473</a>.</p>
-<p>The example usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">context</span> <span class="o">=</span> <span class="n">simple_attention</span><span class="p">(</span><span class="n">encoded_sequence</span><span class="o">=</span><span class="n">enc_seq</span><span class="p">,</span>
-                           <span class="n">encoded_proj</span><span class="o">=</span><span class="n">enc_proj</span><span class="p">,</span>
-                           <span class="n">decoder_state</span><span class="o">=</span><span class="n">decoder_prev</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>name</strong> (<em>basestring</em>) &#8211; name of the attention model.</li>
-<li><strong>softmax_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; parameter attribute of sequence softmax
-that is used to produce attention weight</li>
-<li><strong>weight_act</strong> (<em>Activation</em>) &#8211; activation of the attention model</li>
-<li><strong>encoded_sequence</strong> (<em>LayerOutput</em>) &#8211; output of the encoder</li>
-<li><strong>encoded_proj</strong> (<em>LayerOutput</em>) &#8211; attention weight is computed by a feed forward neural
-network which has two inputs : decoder&#8217;s hidden state
-of previous time step and encoder&#8217;s output.
-encoded_proj is output of the feed-forward network for
-encoder&#8217;s output. Here we pre-compute it outside
-simple_attention for speed consideration.</li>
-<li><strong>decoder_state</strong> (<em>LayerOutput</em>) &#8211; hidden state of decoder in previous time step</li>
-<li><strong>transform_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; parameter attribute of the feed-forward
-network that takes decoder_state as inputs to
-compute attention weight.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">a context vector</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-</div>
-<div class="section" id="miscs">
-<h2>Miscs<a class="headerlink" href="#miscs" title="永久链接至标题">¶</a></h2>
-<div class="section" id="dropout-layer">
-<h3>dropout_layer<a class="headerlink" href="#dropout-layer" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">dropout_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>&#64;TODO(yuyang18): Add comments.</p>
-<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>name</strong> &#8211; </li>
-<li><strong>input</strong> &#8211; </li>
-<li><strong>dropout_rate</strong> &#8211; </li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="outputs">
-<h3>outputs<a class="headerlink" href="#outputs" title="永久链接至标题">¶</a></h3>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">outputs</code><span class="sig-paren">(</span><em>layers</em>, <em>*args</em><span class="sig-paren">)</span></dt>
-<dd><p>Declare the outputs of network. If user have not defined the inputs of
-network, this method will calculate the input order by dfs travel.</p>
-<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"><strong>layers</strong> (<em>list|tuple|LayerOutput</em>) &#8211; Output layers.</td>
-</tr>
-<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"></td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
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-</div>
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-  <div class="section" id="optimizers">
-<span id="api-trainer-config-helpers-optimizers"></span><h1>Optimizers<a class="headerlink" href="#optimizers" title="永久链接至标题">¶</a></h1>
-<div class="section" id="basesgdoptimizer">
-<h2>BaseSGDOptimizer<a class="headerlink" href="#basesgdoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">BaseSGDOptimizer</code></dt>
-<dd><p>SGD Optimizer.</p>
-<p>SGD is an optimization method, trying to find a neural network that
-minimize the &#8220;cost/error&#8221; of it by iteration. In paddle&#8217;s implementation
-SGD Optimizer is synchronized, which means all gradients will be wait to
-calculate and reduced into one gradient, then do optimize operation.</p>
-<p>The neural network consider the learning problem of minimizing an objective
-function, that has the form of a sum</p>
-<div class="math">
-\[Q(w) = \sum_{i}^{n} Q_i(w)\]</div>
-<p>The value of function Q sometimes is the cost of neural network (Mean
-Square Error between prediction and label for example). The function Q is
-parametrised by w, the weight/bias of neural network. And weights is what to
-be learned. The i is the i-th observation in (trainning) data.</p>
-<p>So, the SGD method will optimize the weight by</p>
-<div class="math">
-\[w = w - \eta \nabla Q(w) = w - \eta \sum_{i}^{n} \nabla Q_i(w)\]</div>
-<p>where <span class="math">\(\eta\)</span> is learning rate. And <span class="math">\(n\)</span> is batch size.</p>
-</dd></dl>
-
-</div>
-<div class="section" id="momentumoptimizer">
-<h2>MomentumOptimizer<a class="headerlink" href="#momentumoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">MomentumOptimizer</code><span class="sig-paren">(</span><em>momentum=None</em>, <em>sparse=False</em><span class="sig-paren">)</span></dt>
-<dd><p>MomentumOptimizer.</p>
-<p>When sparse=True, the update scheme:</p>
-<div class="math">
-\[\begin{split}\alpha_t &amp;= \alpha_{t-1} / k \\
-\beta_t &amp;= \beta_{t-1} / (1 + \lambda \gamma_t) \\
-u_t &amp;= u_{t-1} - \alpha_t \gamma_t g_t \\
-v_t &amp;= v_{t-1} + \tau_{t-1} \alpha_t \gamma_t g_t \\
-\tau_t &amp;= \tau_{t-1} + \beta_t / \alpha_t\end{split}\]</div>
-<p>where <span class="math">\(k\)</span> is momentum, <span class="math">\(\lambda\)</span> is decay rate,
-<span class="math">\(\gamma_t\)</span> is learning rate at the t&#8217;th step.</p>
-<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"><strong>sparse</strong> (<em>bool</em>) &#8211; with sparse support or not.</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adamoptimizer">
-<h2>AdamOptimizer<a class="headerlink" href="#adamoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdamOptimizer</code><span class="sig-paren">(</span><em>beta1=0.9</em>, <em>beta2=0.999</em>, <em>epsilon=1e-08</em><span class="sig-paren">)</span></dt>
-<dd><p>Adam optimizer.
-The details of please refer <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
-<div class="math">
-\[\begin{split}m(w, t) &amp; = \beta_1 m(w, t-1) + (1 - \beta_1) \nabla Q_i(w) \\
-v(w, t) &amp; = \beta_2 v(w, t-1) + (1 - \beta_2)(\nabla Q_i(w)) ^2 \\
-w &amp; = w - \frac{\eta}{\sqrt{v(w,t) + \epsilon}}\end{split}\]</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 last simple">
-<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in equation.</li>
-<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in equation.</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in equation. It is used to prevent
-divided by zero.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adamaxoptimizer">
-<h2>AdamaxOptimizer<a class="headerlink" href="#adamaxoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdamaxOptimizer</code><span class="sig-paren">(</span><em>beta1</em>, <em>beta2</em><span class="sig-paren">)</span></dt>
-<dd><p>Adamax optimizer.</p>
-<p>The details of please refer this <a class="reference external" href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
-<div class="math">
-\[\begin{split}m_t &amp; = \beta_1 * m_{t-1} + (1-\beta_1)* \nabla Q_i(w) \\
-u_t &amp; = max(\beta_2*u_{t-1}, abs(\nabla Q_i(w))) \\
-w_t &amp; = w_{t-1} - (\eta/(1-\beta_1^t))*m_t/u_t\end{split}\]</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 last simple">
-<li><strong>beta1</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_1\)</span> in the equation.</li>
-<li><strong>beta2</strong> (<em>float</em>) &#8211; the <span class="math">\(\beta_2\)</span> in the equation.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adagradoptimizer">
-<h2>AdaGradOptimizer<a class="headerlink" href="#adagradoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdaGradOptimizer</code></dt>
-<dd><p>Adagrad(for ADAptive GRAdient algorithm) optimizer.</p>
-<p>For details please refer this <a class="reference external" href="http://www.magicbroom.info/Papers/DuchiHaSi10.pdf">Adaptive Subgradient Methods for
-Online Learning and Stochastic Optimization</a>.</p>
-<div class="math">
-\[\begin{split}G &amp;= \sum_{\tau=1}^{t} g_{\tau} g_{\tau}^T \\
-w &amp; = w - \eta diag(G)^{-\frac{1}{2}} \circ g\end{split}\]</div>
-</dd></dl>
-
-</div>
-<div class="section" id="decayedadagradoptimizer">
-<h2>DecayedAdaGradOptimizer<a class="headerlink" href="#decayedadagradoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">DecayedAdaGradOptimizer</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em><span class="sig-paren">)</span></dt>
-<dd><p>AdaGrad method with decayed sum gradients. The equations of this method
-show as follow.</p>
-<div class="math">
-\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
-learning\_rate &amp;= 1/sqrt( ( E(g_t^2) + \epsilon )\end{split}\]</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 last simple">
-<li><strong>rho</strong> (<em>float</em>) &#8211; The <span class="math">\(\rho\)</span> parameter in that equation</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; The <span class="math">\(\epsilon\)</span> parameter in that equation.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="adadeltaoptimizer">
-<h2>AdaDeltaOptimizer<a class="headerlink" href="#adadeltaoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">AdaDeltaOptimizer</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em><span class="sig-paren">)</span></dt>
-<dd><p>AdaDelta method. The details of adadelta please refer to this
-<a class="reference external" href="http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf">ADADELTA: AN ADAPTIVE LEARNING RATE METHOD</a>.</p>
-<div class="math">
-\[\begin{split}E(g_t^2) &amp;= \rho * E(g_{t-1}^2) + (1-\rho) * g^2 \\
-learning\_rate &amp;= sqrt( ( E(dx_{t-1}^2) + \epsilon ) / ( \
-                  E(g_t^2) + \epsilon ) ) \\
-E(dx_t^2) &amp;= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2\end{split}\]</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 last simple">
-<li><strong>rho</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; <span class="math">\(\rho\)</span> in equation</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="rmspropoptimizer">
-<h2>RMSPropOptimizer<a class="headerlink" href="#rmspropoptimizer" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">RMSPropOptimizer</code><span class="sig-paren">(</span><em>rho=0.95</em>, <em>epsilon=1e-06</em><span class="sig-paren">)</span></dt>
-<dd><p>RMSProp(for Root Mean Square Propagation) optimizer. For details please
-refer this <a class="reference external" href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">slide</a>.</p>
-<p>The equations of this method as follows:</p>
-<div class="math">
-\[\begin{split}v(w, t) &amp; = \rho v(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2 \\
-w &amp; = w - \frac{\eta} {\sqrt{v(w,t) + \epsilon}} \nabla Q_{i}(w)\end{split}\]</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 last simple">
-<li><strong>rho</strong> (<em>float</em>) &#8211; the <span class="math">\(\rho\)</span> in the equation. The forgetting factor.</li>
-<li><strong>epsilon</strong> (<em>float</em>) &#8211; the <span class="math">\(\epsilon\)</span> in the equation.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
-<div class="section" id="settings">
-<span id="api-trainer-config-helpers-optimizers-settings"></span><h2>settings<a class="headerlink" href="#settings" title="永久链接至标题">¶</a></h2>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.optimizers.</code><code class="descname">settings</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
-<dd><p>Set the optimization method, learning rate, batch size, and other training
-settings. The currently supported algorithms are SGD and Async-SGD.</p>
-<div class="admonition warning">
-<p class="first admonition-title">警告</p>
-<p class="last">Note that the &#8216;batch_size&#8217; in PaddlePaddle is not equal to global
-training batch size. It represents the single training process&#8217;s batch
-size. If you use N processes to train one model, for example use three
-GPU machines, the global batch size is N*&#8217;batch_size&#8217;.</p>
-</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 last simple">
-<li><strong>batch_size</strong> (<em>int</em>) &#8211; batch size for one training process.</li>
-<li><strong>learning_rate</strong> (<em>float</em>) &#8211; learning rate for SGD</li>
-<li><strong>learning_method</strong> (<em>BaseSGDOptimizer</em>) &#8211; The extension optimization algorithms of gradient
-descent, such as momentum, adagrad, rmsprop, etc.
-Note that it should be instance with base type
-BaseSGDOptimizer.</li>
-<li><strong>regularization</strong> (<em>BaseRegularization</em>) &#8211; The regularization method.</li>
-<li><strong>is_async</strong> (<em>bool</em>) &#8211; Is Async-SGD or not. Default value is False.</li>
-<li><strong>model_average</strong> (<em>ModelAverage</em>) &#8211; Model Average Settings.</li>
-<li><strong>gradient_clipping_threshold</strong> (<em>float</em>) &#8211; gradient clipping threshold. If gradient
-value larger than some value, will be
-clipped.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
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-<li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">模型配置</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/attr.html">Parameter Attribute</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/data.html">数据访问</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/run_logic.html">训练与应用</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../../faq/index_cn.html">FAQ</a></li>
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-
-        
-    </nav>
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-<div role="navigation" aria-label="breadcrumbs navigation">
-  <ul class="wy-breadcrumbs">
-      
-    <li>Poolings</li>
-  </ul>
-</div>
-      
-      <div class="wy-nav-content" id="doc-content">
-        <div class="rst-content">
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-            
-  <div class="section" id="poolings">
-<h1>Poolings<a class="headerlink" href="#poolings" title="永久链接至标题">¶</a></h1>
-<div class="section" id="basepoolingtype">
-<h2>BasePoolingType<a class="headerlink" href="#basepoolingtype" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">BasePoolingType</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">BasePool</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="avgpooling">
-<h2>AvgPooling<a class="headerlink" href="#avgpooling" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">AvgPooling</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Avg</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="maxpooling">
-<h2>MaxPooling<a class="headerlink" href="#maxpooling" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">MaxPooling</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Max</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="sumpooling">
-<h2>SumPooling<a class="headerlink" href="#sumpooling" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">SumPooling</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">Sum</span></code> 的别名</p>
-</dd></dl>
-
-</div>
-<div class="section" id="squarerootnpooling">
-<h2>SquareRootNPooling<a class="headerlink" href="#squarerootnpooling" title="永久链接至标题">¶</a></h2>
-<dl class="attribute">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.poolings.</code><code class="descname">SquareRootNPooling</code></dt>
-<dd><p><code class="xref py py-class docutils literal"><span class="pre">SquareRootN</span></code> 的别名</p>
-</dd></dl>
-
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develop/doc_cn/api/v2/config/activation.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">模型配置</a><ul class="current">
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>

develop/doc_cn/api/v2/config/attr.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">模型配置</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>

develop/doc_cn/api/v1/trainer_config_helpers/evaluators.html → develop/doc_cn/api/v2/config/evaluators.html

@@ -32,7 +32,10 @@
         <link rel="index" title="索引"
               href="../../../genindex.html"/>
         <link rel="search" title="搜索" href="../../../search.html"/>
-    <link rel="top" title="PaddlePaddle  文档" href="../../../index.html"/> 
+    <link rel="top" title="PaddlePaddle  文档" href="../../../index.html"/>
+        <link rel="up" title="Model Configuration" href="../model_configs.html"/>
+        <link rel="next" title="Optimizer" href="optimizer.html"/>
+        <link rel="prev" title="Layers" href="layer.html"/> 
 
   <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
   <link rel="stylesheet" href="../../../_static/css/override.css" type="text/css" />
@@ -80,10 +83,10 @@
       </div>
       <div class="doc-module">
         
-        <ul>
+        <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../../getstarted/index_cn.html">新手入门</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../howto/index_cn.html">进阶指南</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../index_cn.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index_cn.html">API</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../faq/index_cn.html">FAQ</a></li>
 </ul>
 
@@ -105,7 +108,7 @@
     <nav class="doc-menu-vertical" role="navigation">
         
           
-          <ul>
+          <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../../getstarted/index_cn.html">新手入门</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../getstarted/build_and_install/index_cn.html">安装与编译</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../getstarted/build_and_install/docker_install_cn.html">PaddlePaddle的Docker容器使用方式</a></li>
@@ -138,18 +141,19 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../howto/optimization/gpu_profiling_cn.html">GPU性能分析与调优</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../index_cn.html">API</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">模型配置</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../v2/config/attr.html">Parameter Attribute</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index_cn.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">模型配置</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Evaluators</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
+<li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="attr.html">Parameter Attribute</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/data.html">数据访问</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../v2/run_logic.html">训练与应用</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../data.html">数据访问</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../run_logic.html">训练与应用</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../faq/index_cn.html">FAQ</a></li>
@@ -173,6 +177,10 @@
 <div role="navigation" aria-label="breadcrumbs navigation">
   <ul class="wy-breadcrumbs">
       
+        <li><a href="../../index_cn.html">API</a> > </li>
+      
+        <li><a href="../model_configs.html">Model Configuration</a> > </li>
+      
     <li>Evaluators</li>
   </ul>
 </div>
@@ -183,54 +191,14 @@
            <div itemprop="articleBody">
             
   <div class="section" id="evaluators">
-<span id="api-trainer-config-helpers-evaluators"></span><h1>Evaluators<a class="headerlink" href="#evaluators" title="永久链接至标题">¶</a></h1>
-<div class="section" id="base">
-<h2>Base<a class="headerlink" href="#base" title="永久链接至标题">¶</a></h2>
-<dl class="function">
-<dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">evaluator_base</code><span class="sig-paren">(</span><em>input</em>, <em>type</em>, <em>label=None</em>, <em>weight=None</em>, <em>name=None</em>, <em>chunk_scheme=None</em>, <em>num_chunk_types=None</em>, <em>classification_threshold=None</em>, <em>positive_label=None</em>, <em>dict_file=None</em>, <em>result_file=None</em>, <em>num_results=None</em>, <em>delimited=None</em>, <em>top_k=None</em>, <em>excluded_chunk_types=None</em><span class="sig-paren">)</span></dt>
-<dd><p>Evaluator will evaluate the network status while training/testing.</p>
-<p>User can use evaluator by classify/regression job. For example.</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">classify</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">evaluator</span><span class="o">=</span><span class="n">classification_error_evaluator</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>And user could define evaluator separately as follow.</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">classification_error_evaluator</span><span class="p">(</span><span class="s2">&quot;ErrorRate&quot;</span><span class="p">,</span> <span class="n">prediction</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The evaluator often contains a name parameter. It will also be printed when
-evaluating network. The printed information may look like the following.</p>
-<div class="highlight-text"><div class="highlight"><pre><span></span>Batch=200 samples=20000 AvgCost=0.679655 CurrentCost=0.662179 Eval:
-classification_error_evaluator=0.4486
-CurrentEval: ErrorRate=0.3964
-</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 last simple">
-<li><strong>input</strong> (<em>list|LayerOutput</em>) &#8211; Input layers, a object of LayerOutput or a list of
-LayerOutput.</li>
-<li><strong>label</strong> (<em>LayerOutput|None</em>) &#8211; An input layer containing the ground truth label.</li>
-<li><strong>weight</strong> (<em>LayerOutput.</em>) &#8211; An input layer which is a weight for each sample.
-Each evaluator may calculate differently to use this weight.</li>
-<li><strong>top_k</strong> (<em>int</em>) &#8211; number k in top-k error rate</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</div>
+<span id="api-v2"></span><h1>Evaluators<a class="headerlink" href="#evaluators" title="永久链接至标题">¶</a></h1>
 <div class="section" id="classification">
 <h2>Classification<a class="headerlink" href="#classification" title="永久链接至标题">¶</a></h2>
-<div class="section" id="classification-error-evaluator">
-<h3>classification_error_evaluator<a class="headerlink" href="#classification-error-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="classification-error">
+<h3>classification_error<a class="headerlink" href="#classification-error" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">classification_error_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">classification_error</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Classification Error Evaluator. It will print error rate for classification.</p>
 <p>The classification error is:</p>
 <div class="math">
@@ -245,9 +213,9 @@ Each evaluator may calculate differently to use this weight.</li>
 <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>name</strong> (<em>basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
 <li><strong>label</strong> (<em>basestring</em>) &#8211; Label layer name.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. And will just multiply to NumOfWrongPredicts
 and NumOfAllSamples. So, the elements of weight are all one,
 then means not set weight. The larger weight it is, the more
@@ -265,11 +233,11 @@ important this sample is.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="auc-evaluator">
-<h3>auc_evaluator<a class="headerlink" href="#auc-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="auc">
+<h3>auc<a class="headerlink" href="#auc" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">auc_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">auc</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Auc Evaluator which adapts to binary classification.</p>
 <p>The simple usage:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">auc_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
@@ -281,9 +249,9 @@ important this sample is.</li>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
 <li><strong>label</strong> (<em>None|basestring</em>) &#8211; Label layer name.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1].</li>
 </ul>
 </td>
@@ -293,11 +261,11 @@ important this sample is.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="ctc-error-evaluator">
-<h3>ctc_error_evaluator<a class="headerlink" href="#ctc-error-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="ctc-error">
+<h3>ctc_error<a class="headerlink" href="#ctc-error" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">ctc_error_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">ctc_error</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This evaluator is to calculate sequence-to-sequence edit distance.</p>
 <p>The simple usage is :</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">ctc_error_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">lbl</span><span class="p">)</span>
@@ -309,9 +277,9 @@ important this sample is.</li>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer. Should be the same as the input for ctc_layer.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; input label, which is a data_layer. Should be the same as the
-label for ctc_layer</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer. Should be the same as the input for ctc.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input label, which is a data. Should be the same as the
+label for ctc</li>
 </ul>
 </td>
 </tr>
@@ -320,11 +288,11 @@ label for ctc_layer</li>
 </dd></dl>
 
 </div>
-<div class="section" id="chunk-evaluator">
-<h3>chunk_evaluator<a class="headerlink" href="#chunk-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="chunk">
+<h3>chunk<a class="headerlink" href="#chunk" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">chunk_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">chunk</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Chunk evaluator is used to evaluate segment labelling accuracy for a
 sequence. It calculates the chunk detection F1 score.</p>
 <p>A chunk is correctly detected if its beginning, end and type are correct.
@@ -355,8 +323,8 @@ The tag type for each of the scheme is shown as follows:</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; The input layers.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; An input layer containing the ground truth label.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layers.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; An input layer containing the ground truth label.</li>
 <li><strong>chunk_scheme</strong> (<em>basestring</em>) &#8211; The labelling schemes support 4 types. It is one of
 &#8220;IOB&#8221;, &#8220;IOE&#8221;, &#8220;IOBES&#8221;, &#8220;plain&#8221;. It is required.</li>
 <li><strong>num_chunk_types</strong> &#8211; number of chunk types other than &#8220;other&#8221;</li>
@@ -370,11 +338,11 @@ The tag type for each of the scheme is shown as follows:</p>
 </dd></dl>
 
 </div>
-<div class="section" id="precision-recall-evaluator">
-<h3>precision_recall_evaluator<a class="headerlink" href="#precision-recall-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="precision-recall">
+<h3>precision_recall<a class="headerlink" href="#precision-recall" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">precision_recall_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">precision_recall</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>An Evaluator to calculate precision and recall, F1-score.
 It is adapt to the task with multiple labels.</p>
 <ul class="simple">
@@ -393,10 +361,10 @@ F1-score of this label.</li>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Label layer name.</li>
-<li><strong>positive_label</strong> (<em>LayerOutput.</em>) &#8211; The input label layer.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label layer name.</li>
+<li><strong>positive_label</strong> (<em>paddle.v2.config_base.Layer.</em>) &#8211; The input label layer.</li>
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. (TODO, explaination)</li>
 </ul>
 </td>
@@ -409,11 +377,11 @@ F1-score of this label.</li>
 </div>
 <div class="section" id="rank">
 <h2>Rank<a class="headerlink" href="#rank" title="永久链接至标题">¶</a></h2>
-<div class="section" id="pnpair-evaluator">
-<h3>pnpair_evaluator<a class="headerlink" href="#pnpair-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="pnpair">
+<h3>pnpair<a class="headerlink" href="#pnpair" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">pnpair_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">pnpair</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Positive-negative pair rate Evaluator which adapts to rank task like
 learning to rank. This evaluator must contain at least three layers.</p>
 <p>The simple usage:</p>
@@ -426,10 +394,10 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name. The output prediction of network.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Label layer name.</li>
-<li><strong>info</strong> (<em>LayerOutput</em>) &#8211; Label layer name. (TODO, explaination)</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name. The output prediction of network.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label layer name.</li>
+<li><strong>info</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label layer name. (TODO, explaination)</li>
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. (TODO, explaination)</li>
 </ul>
 </td>
@@ -442,11 +410,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </div>
 <div class="section" id="utils">
 <h2>Utils<a class="headerlink" href="#utils" title="永久链接至标题">¶</a></h2>
-<div class="section" id="sum-evaluator">
-<h3>sum_evaluator<a class="headerlink" href="#sum-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="sum">
+<h3>sum<a class="headerlink" href="#sum" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">sum_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">sum</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>An Evaluator to sum the result of input.</p>
 <p>The simple usage:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">sum_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
@@ -458,8 +426,8 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name.</li>
-<li><strong>weight</strong> (<em>LayerOutput</em>) &#8211; Weight Layer name. It should be a matrix with size
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name.</li>
+<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight Layer name. It should be a matrix with size
 [sample_num, 1]. (TODO, explaination)</li>
 </ul>
 </td>
@@ -469,11 +437,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="column-sum-evaluator">
-<h3>column_sum_evaluator<a class="headerlink" href="#column-sum-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="column-sum">
+<h3>column_sum<a class="headerlink" href="#column-sum" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">column_sum_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">column_sum</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to sum the last column of input.</p>
 <p>The simple usage is:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">column_sum_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
@@ -485,7 +453,7 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first last simple">
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input Layer name.</li>
 </ul>
 </td>
 </tr>
@@ -497,11 +465,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </div>
 <div class="section" id="print">
 <h2>Print<a class="headerlink" href="#print" title="永久链接至标题">¶</a></h2>
-<div class="section" id="classification-error-printer-evaluator">
-<h3>classification_error_printer_evaluator<a class="headerlink" href="#classification-error-printer-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="classification-error-printer">
+<h3>classification_error_printer<a class="headerlink" href="#classification-error-printer" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">classification_error_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">classification_error_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the classification error of each sample.</p>
 <p>The simple usage is:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">classification_error_printer_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
@@ -512,8 +480,8 @@ learning to rank. This evaluator must contain at least three layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; Input layer.</li>
-<li><strong>label</strong> (<em>LayerOutput</em>) &#8211; Input label layer.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer.</li>
+<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input label layer.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -523,11 +491,11 @@ learning to rank. This evaluator must contain at least three layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="gradient-printer-evaluator">
-<h3>gradient_printer_evaluator<a class="headerlink" href="#gradient-printer-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="gradient-printer">
+<h3>gradient_printer<a class="headerlink" href="#gradient-printer" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">gradient_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">gradient_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the gradient of input layers. It contains
 one or more input layers.</p>
 <p>The simple usage is:</p>
@@ -539,7 +507,7 @@ one or more input layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; One or more input layers.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; One or more input layers.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -549,11 +517,11 @@ one or more input layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="maxid-printer-evaluator">
-<h3>maxid_printer_evaluator<a class="headerlink" href="#maxid-printer-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="maxid-printer">
+<h3>maxid_printer<a class="headerlink" href="#maxid-printer" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">maxid_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">maxid_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print maximum top k values and their indexes
 of each row of input layers. It contains one or more input layers.
 k is specified by num_results.</p>
@@ -566,7 +534,7 @@ k is specified by num_results.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; Input Layer name.</li>
 <li><strong>num_results</strong> (<em>int.</em>) &#8211; This number is used to specify the top k numbers.
 It is 1 by default.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
@@ -578,11 +546,11 @@ It is 1 by default.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="maxframe-printer-evaluator">
-<h3>maxframe_printer_evaluator<a class="headerlink" href="#maxframe-printer-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="maxframe-printer">
+<h3>maxframe_printer<a class="headerlink" href="#maxframe-printer" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">maxframe_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">maxframe_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the top k frames of each input layers.
 The input layers should contain sequences info or sequences type.
 k is specified by num_results.
@@ -600,7 +568,7 @@ It contains one or more input layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; Input Layer name.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -610,11 +578,11 @@ It contains one or more input layers.</p>
 </dd></dl>
 
 </div>
-<div class="section" id="seqtext-printer-evaluator">
-<h3>seqtext_printer_evaluator<a class="headerlink" href="#seqtext-printer-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="seqtext-printer">
+<h3>seqtext_printer<a class="headerlink" href="#seqtext-printer" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">seqtext_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">seqtext_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>Sequence text printer will print text according to index matrix and a
 dictionary. There can be multiple input to this layer:</p>
 <p>1. If there is no id_input, the input must be a matrix containing
@@ -646,7 +614,7 @@ the sequence of indices;</p>
 <p>Typically SequenceTextPrinter layer takes output of maxid or RecurrentGroup
 with maxid (when generating) as an input.</p>
 <p>The simple usage is:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">seqtext_printer_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">maxid_layer</span><span class="p">,</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span> <span class="o">=</span> <span class="n">seqtext_printer_evaluator</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">maxid</span><span class="p">,</span>
                                  <span class="n">id_input</span><span class="o">=</span><span class="n">sample_id</span><span class="p">,</span>
                                  <span class="n">dict_file</span><span class="o">=</span><span class="n">dict_file</span><span class="p">,</span>
                                  <span class="n">result_file</span><span class="o">=</span><span class="n">result_file</span><span class="p">)</span>
@@ -657,9 +625,9 @@ with maxid (when generating) as an input.</p>
 <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|list</em>) &#8211; Input Layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; Input Layer name.</li>
 <li><strong>result_file</strong> (<em>basestring</em>) &#8211; Path of the file to store the generated results.</li>
-<li><strong>id_input</strong> (<em>LayerOutput</em>) &#8211; Index of the input sequence, and the specified index will
+<li><strong>id_input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Index of the input sequence, and the specified index will
 be prited in the gereated results. This an optional
 parameter.</li>
 <li><strong>dict_file</strong> (<em>basestring</em>) &#8211; Path of dictionary. This is an optional parameter.
@@ -684,11 +652,11 @@ Default is True. No space is added if set to False.</li>
 </dd></dl>
 
 </div>
-<div class="section" id="value-printer-evaluator">
-<h3>value_printer_evaluator<a class="headerlink" href="#value-printer-evaluator" title="永久链接至标题">¶</a></h3>
-<dl class="function">
+<div class="section" id="value-printer">
+<h3>value_printer<a class="headerlink" href="#value-printer" title="永久链接至标题">¶</a></h3>
+<dl class="class">
 <dt>
-<code class="descclassname">paddle.trainer_config_helpers.evaluators.</code><code class="descname">value_printer_evaluator</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.evaluator.</code><code class="descname">value_printer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This Evaluator is used to print the values of input layers. It contains
 one or more input layers.</p>
 <p>The simple usage is:</p>
@@ -700,7 +668,7 @@ one or more input layers.</p>
 <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 last simple">
-<li><strong>input</strong> (<em>LayerOutput|list</em>) &#8211; One or more input layers.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list</em>) &#8211; One or more input layers.</li>
 <li><strong>name</strong> (<em>None|basestring</em>) &#8211; Evaluator name.</li>
 </ul>
 </td>
@@ -718,6 +686,15 @@ one or more input layers.</p>
           </div>
           <footer>
   
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="optimizer.html" class="btn btn-neutral float-right" title="Optimizer" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+      
+      
+        <a href="layer.html" class="btn btn-neutral" title="Layers" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+      
+    </div>
+  
 
   <hr/>
 

develop/doc_cn/api/v2/config/layer.html

@@ -34,7 +34,7 @@
         <link rel="search" title="搜索" href="../../../search.html"/>
     <link rel="top" title="PaddlePaddle  文档" href="../../../index.html"/>
         <link rel="up" title="Model Configuration" href="../model_configs.html"/>
-        <link rel="next" title="Optimizer" href="optimizer.html"/>
+        <link rel="next" title="Evaluators" href="evaluators.html"/>
         <link rel="prev" title="Activation" href="activation.html"/> 
 
   <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
@@ -145,6 +145,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">模型配置</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>
@@ -1460,10 +1461,10 @@ Each inputs is a projection or operator.</p>
 <li><strong>input</strong> &#8211; inputs layer. It is an optional parameter. If set,
 then this function will just return layer&#8217;s name.</li>
 <li><strong>act</strong> (<em>BaseActivation</em>) &#8211; Activation Type.</li>
-<li><strong>bias_attr</strong> (<a class="reference internal" href="../../v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a><em> or </em><em>None</em><em> or </em><em>bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
+<li><strong>bias_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None</em><em> or </em><em>bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
 something not type of ParameterAttribute. None will get a
 default Bias.</li>
-<li><strong>layer_attr</strong> (<a class="reference internal" href="../../v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; The extra layer config. Default is None.</li>
+<li><strong>layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; The extra layer config. Default is None.</li>
 </ul>
 </td>
 </tr>
@@ -3477,7 +3478,7 @@ It is used by recurrent layer group.</p>
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="optimizer.html" class="btn btn-neutral float-right" title="Optimizer" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="evaluators.html" class="btn btn-neutral float-right" title="Evaluators" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="activation.html" class="btn btn-neutral" title="Activation" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>

develop/doc_cn/api/v2/config/networks.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">模型配置</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Networks</a></li>
@@ -416,6 +417,9 @@ False if no bias.</li>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="small-vgg">
+<h3>small_vgg<a class="headerlink" href="#small-vgg" title="永久链接至标题">¶</a></h3>
 </div>
 <div class="section" id="vgg-16-network">
 <h3>vgg_16_network<a class="headerlink" href="#vgg-16-network" title="永久链接至标题">¶</a></h3>
@@ -798,6 +802,86 @@ gru_group, and gru_group is relatively better than simple_gru.</p>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="simple-gru2">
+<h4>simple_gru2<a class="headerlink" href="#simple-gru2" title="永久链接至标题">¶</a></h4>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.networks.</code><code class="descname">simple_gru2</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>simple_gru2 is the same with simple_gru, but using grumemory instead
+Please see grumemory in layers.py for more detail about the maths.
+simple_gru2 is faster than simple_gru.</p>
+<p>The example usage is:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">simple_gru2</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</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>paddle.v2.config_base.Layer</em>) &#8211; input layer name.</li>
+<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
+<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
+<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
+<li><strong>act</strong> (<em>paddle.v2.Activation.Base</em>) &#8211; type of the activiation</li>
+<li><strong>gate_act</strong> (<em>paddle.v2.Activation.Base</em>) &#8211; type of the gate activiation</li>
+<li><strong>gru_bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
+<li><strong>gru_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru group.</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">paddle.v2.config_base.Layer</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+<div class="section" id="bidirectional-gru">
+<h4>bidirectional_gru<a class="headerlink" href="#bidirectional-gru" title="永久链接至标题">¶</a></h4>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.networks.</code><code class="descname">bidirectional_gru</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>A bidirectional_gru is a recurrent unit that iterates over the input
+sequence both in forward and bardward orders, and then concatenate two
+outputs to form a final output. However, concatenation of two outputs
+is not the only way to form the final output, you can also, for example,
+just add them together.</p>
+<p>The example usage is:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">bi_gru</span> <span class="o">=</span> <span class="n">bidirectional_gru</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</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>name</strong> (<em>basestring</em>) &#8211; bidirectional gru layer name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer.</li>
+<li><strong>size</strong> (<em>int</em>) &#8211; gru layer size.</li>
+<li><strong>return_seq</strong> (<em>bool</em>) &#8211; If set False, outputs of the last time step are
+concatenated and returned.
+If set True, the entire output sequences that are
+processed in forward and backward directions are
+concatenated and returned.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer 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">paddle.v2.config_base.Layer</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </div>
 </div>
 <div class="section" id="simple-attention">

develop/doc_cn/api/v2/config/optimizer.html

@@ -35,7 +35,7 @@
     <link rel="top" title="PaddlePaddle  文档" href="../../../index.html"/>
         <link rel="up" title="Model Configuration" href="../model_configs.html"/>
         <link rel="next" title="Pooling" href="pooling.html"/>
-        <link rel="prev" title="Layers" href="layer.html"/> 
+        <link rel="prev" title="Evaluators" href="evaluators.html"/> 
 
   <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
   <link rel="stylesheet" href="../../../_static/css/override.css" type="text/css" />
@@ -145,6 +145,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">模型配置</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>
@@ -374,7 +375,7 @@ w &amp; = w - \frac{\eta} {\sqrt{v(w,t) + \epsilon}} \nabla Q_{i}(w)\end{split}\
         <a href="pooling.html" class="btn btn-neutral float-right" title="Pooling" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="layer.html" class="btn btn-neutral" title="Layers" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="evaluators.html" class="btn btn-neutral" title="Evaluators" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   

develop/doc_cn/api/v2/config/pooling.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2 current"><a class="reference internal" href="../model_configs.html">模型配置</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="networks.html">Networks</a></li>

develop/doc_cn/api/v2/data.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/networks.html">Networks</a></li>

develop/doc_cn/api/v2/model_configs.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2 current"><a class="current reference internal" href="#">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/networks.html">Networks</a></li>
@@ -193,6 +194,7 @@
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l1"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l1"><a class="reference internal" href="config/networks.html">Networks</a></li>

develop/doc_cn/api/v2/run_logic.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="config/networks.html">Networks</a></li>
@@ -501,8 +502,9 @@ index that reader returns.</li>
 <dd><p>Infer a neural network by given neural network output and parameters.  The
 user should pass either a batch of input data or reader method.</p>
 <p>Example usages:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">SomeData</span><span class="p">,</span>
-                      <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">outptut_layer</span><span class="o">=</span><span class="n">prediction</span><span class="p">,</span>
+                      <span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span>
+                      <span class="nb">input</span><span class="o">=</span><span class="n">SomeData</span><span class="p">)</span>
 <span class="k">print</span> <span class="n">result</span>
 </pre></div>
 </div>

develop/doc_cn/design/api.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/design/cluster_train/README.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/design/cluster_train/checkpointing.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/design/cluster_train/data_dispatch.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/design/multi_language_interface/00.why_plain_c.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/design/multi_language_interface/01.inference_implementation.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/design/reader/README.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/design/releasing_process.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/faq/index_cn.html

@@ -143,6 +143,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/genindex.html

@@ -143,6 +143,7 @@
 <li class="toctree-l2"><a class="reference internal" href="api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/networks.html">Networks</a></li>
@@ -187,56 +188,8 @@
 <h1 id="index">索引</h1>
 
 <div class="genindex-jumpbox">
- <a href="#D"><strong>D</strong></a>
- | <a href="#E"><strong>E</strong></a>
- | <a href="#P"><strong>P</strong></a>
- | <a href="#S"><strong>S</strong></a>
  
 </div>
-<h2 id="D">D</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/data_sources.html#paddle.trainer_config_helpers.data_sources.define_py_data_sources2">define_py_data_sources2() (在 paddle.trainer_config_helpers.data_sources 模块中)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ExtraAttr">ExtraAttr() (在 paddle.trainer_config_helpers.attrs 模块中)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute">ExtraLayerAttribute (paddle.trainer_config_helpers.attrs 中的类)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="P">P</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#module-paddle.trainer_config_helpers.attrs">paddle.trainer_config_helpers.attrs (模块)</a>
-</li>
-      <li><a href="api/v1/trainer_config_helpers/data_sources.html#module-paddle.trainer_config_helpers.data_sources">paddle.trainer_config_helpers.data_sources (模块)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParamAttr">ParamAttr() (在 paddle.trainer_config_helpers.attrs 模块中)</a>
-</li>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute">ParameterAttribute (paddle.trainer_config_helpers.attrs 中的类)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api/v1/trainer_config_helpers/attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute.set_default_parameter_name">set_default_parameter_name() (paddle.trainer_config_helpers.attrs.ParameterAttribute 方法)</a>
-</li>
-  </ul></td>
-</tr></table>
-
 
 
            </div>

develop/doc_cn/getstarted/basic_usage/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/getstarted/build_and_install/cmake/build_from_source_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/getstarted/build_and_install/docker_install_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/getstarted/build_and_install/index_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/getstarted/build_and_install/ubuntu_install_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/getstarted/index_cn.html

@@ -144,6 +144,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/deep_model/rnn/hierarchical_layer_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/deep_model/rnn/hrnn_rnn_api_compare_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/deep_model/rnn/index_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/deep_model/rnn/recurrent_group_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/deep_model/rnn/rnn_config_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>
@@ -286,7 +287,7 @@ layer(引导层)</strong>，其输出被用作Memory的初始值。
 用于计算编码向量的加权和。加权和用来生成 <span class="math">\(y_t\)</span> 。</p>
 <p>模型的编码器部分如下所示。它叫做<code class="docutils literal"><span class="pre">grumemory</span></code>来表示门控循环神经网络。如果网络架构简单，那么推荐使用循环神经网络的方法，因为它比
 <code class="docutils literal"><span class="pre">recurrent_group</span></code>
-更快。我们已经实现了大多数常用的循环神经网络架构，可以参考 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers"><span class="std std-ref">Layers</span></a> 了解更多细节。</p>
+更快。我们已经实现了大多数常用的循环神经网络架构，可以参考 <span class="xref std std-ref">api_trainer_config_helpers_layers</span> 了解更多细节。</p>
 <p>我们还将编码向量投射到 <code class="docutils literal"><span class="pre">decoder_size</span></code>
 维空间。这通过获得反向循环网络的第一个实例，并将其投射到
 <code class="docutils literal"><span class="pre">decoder_size</span></code> 维空间完成：</p>

develop/doc_cn/howto/dev/contribute_to_paddle_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/dev/new_layer_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/dev/write_docs_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/index_cn.html

@@ -144,6 +144,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/optimization/gpu_profiling_cn.html

@@ -145,6 +145,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/usage/cluster/cluster_train_cn.html

@@ -145,6 +145,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/usage/cmd_parameter/arguments_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc_cn/howto/usage/cmd_parameter/detail_introduction_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc_cn/howto/usage/cmd_parameter/index_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc_cn/howto/usage/cmd_parameter/use_case_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/howto/usage/concepts/use_concepts_cn.html

@@ -145,6 +145,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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@@ -304,7 +305,7 @@
 </div>
 <div class="section" id="id6">
 <h3><a class="toc-backref" href="#id14">优化算法配置</a><a class="headerlink" href="#id6" title="永久链接至标题">¶</a></h3>
-<p>通过 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers-settings"><span class="std std-ref">settings</span></a> 接口设置神经网络所使用的训练参数和 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers"><span class="std std-ref">Optimizers</span></a> ，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers-settings"><span class="std std-ref">settings</span></a> 文档。</p>
+<p>通过 <span class="xref std std-ref">api_trainer_config_helpers_optimizers_settings</span> 接口设置神经网络所使用的训练参数和 <span class="xref std std-ref">api_trainer_config_helpers_optimizers</span> ，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 <span class="xref std std-ref">api_trainer_config_helpers_optimizers_settings</span> 文档。</p>
 </div>
 <div class="section" id="id7">
 <h3><a class="toc-backref" href="#id15">网络结构配置</a><a class="headerlink" href="#id7" title="永久链接至标题">¶</a></h3>
@@ -319,13 +320,13 @@
 </ul>
 <p>这个配置文件网络由 <code class="docutils literal"><span class="pre">data_layer</span></code> 、 <code class="docutils literal"><span class="pre">simple_img_conv_pool</span></code> 、 <code class="docutils literal"><span class="pre">fc_layer</span></code> 组成。</p>
 <ul class="simple">
-<li><a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-data-layer"><span class="std std-ref">data_layer</span></a>  ： 通常每个配置文件都会包括 <code class="docutils literal"><span class="pre">data_layer</span></code> ，定义输入数据大小。</li>
+<li><span class="xref std std-ref">api_trainer_config_helpers_layers_data_layer</span>  ： 通常每个配置文件都会包括 <code class="docutils literal"><span class="pre">data_layer</span></code> ，定义输入数据大小。</li>
 <li><a class="reference internal" href="../../../api/v2/config/networks.html#api-trainer-config-helpers-network-simple-img-conv-pool"><span class="std std-ref">simple_img_conv_pool</span></a> ：是一个组合层，包括了图像的卷积 (convolution)和池化(pooling)。</li>
-<li><a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-fc-layer"><span class="std std-ref">fc_layer</span></a> ：全连接层，激活函数为Softmax，这里也可叫分类层。</li>
+<li><span class="xref std std-ref">api_trainer_config_helpers_layers_fc_layer</span> ：全连接层，激活函数为Softmax，这里也可叫分类层。</li>
 </ul>
 </li>
 <li><p class="first">损失函数和评估器：损失函数即为网络的优化目标，评估器可以评价模型结果。</p>
-<p>PaddlePaddle包括很多损失函数和评估起，详细可以参考 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-cost-layers"><span class="std std-ref">Cost Layers</span></a> 和 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/evaluators.html#api-trainer-config-helpers-evaluators"><span class="std std-ref">Evaluators</span></a> 。这里 <code class="docutils literal"><span class="pre">classification_cost</span></code> 默认使用多类交叉熵损失函数和分类错误率统计评估器。</p>
+<p>PaddlePaddle包括很多损失函数和评估起，详细可以参考 <span class="xref std std-ref">api_trainer_config_helpers_layers_cost_layers</span> 和 <span class="xref std std-ref">api_trainer_config_helpers_evaluators</span> 。这里 <code class="docutils literal"><span class="pre">classification_cost</span></code> 默认使用多类交叉熵损失函数和分类错误率统计评估器。</p>
 </li>
 <li><p class="first"><code class="docutils literal"><span class="pre">outputs</span></code>: 标记网络输出的函数为 <code class="docutils literal"><span class="pre">outputs</span></code> 。</p>
 <p>训练阶段，网络的输出为神经网络的优化目标；预测阶段，网络的输出也可通过 <code class="docutils literal"><span class="pre">outputs</span></code> 标记。</p>
@@ -338,7 +339,7 @@
     <span class="n">out</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>PaddlePaddle 可以使用 <code class="docutils literal"><span class="pre">mixed</span> <span class="pre">layer</span></code> 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-mixed-layer"><span class="std std-ref">mixed_layer</span></a> 的相关文档进行配置。</p>
+<p>PaddlePaddle 可以使用 <code class="docutils literal"><span class="pre">mixed</span> <span class="pre">layer</span></code> 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 <span class="xref std std-ref">api_trainer_config_helpers_layers_mixed_layer</span> 的相关文档进行配置。</p>
 </div>
 </div>
 <div class="section" id="id8">

develop/doc_cn/howto/usage/k8s/k8s_basis_cn.html

@@ -145,6 +145,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc_cn/howto/usage/k8s/k8s_cn.html

@@ -145,6 +145,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc_cn/howto/usage/k8s/k8s_distributed_cn.html

@@ -145,6 +145,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../api/v2/config/optimizer.html">Optimizer</a></li>
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develop/doc_cn/howto/usage/k8s/src/k8s_data/README.html

@@ -142,6 +142,7 @@
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develop/doc_cn/howto/usage/k8s/src/k8s_train/README.html

@@ -142,6 +142,7 @@
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develop/doc_cn/index_cn.html

@@ -143,6 +143,7 @@
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 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/layer.html">Layers</a></li>
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develop/doc_cn/py-modindex.html

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-<li class="toctree-l2"><a class="reference internal" href="howto/usage/cluster/cluster_train_cn.html">运行分布式训练</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="howto/dev/contribute_to_paddle_cn.html">如何贡献代码</a></li>
-<li class="toctree-l2"><a class="reference internal" href="howto/deep_model/rnn/index_cn.html">RNN相关模型</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="howto/deep_model/rnn/recurrent_group_cn.html">Recurrent Group教程</a></li>
-<li class="toctree-l3"><a class="reference internal" href="howto/deep_model/rnn/hierarchical_layer_cn.html">支持双层序列作为输入的Layer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="howto/deep_model/rnn/hrnn_rnn_api_compare_cn.html">单双层RNN API对比介绍</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api/v2/config/pooling.html">Pooling</a></li>
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develop/doc_cn/search.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/embedding_model/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/image_classification/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/imagenet_model/resnet_model_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/quick_start/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>
@@ -413,7 +414,7 @@
 <blockquote>
 <div><a class="reference internal image-reference" href="../../_images/PipelineNetwork_cn.jpg"><img alt="../../_images/PipelineNetwork_cn.jpg" class="align-center" src="../../_images/PipelineNetwork_cn.jpg" style="width: 544.8px; height: 44.8px;" /></a>
 </div></blockquote>
-<p>我们将以最基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置连接请参考 <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers"><span class="std std-ref">Layers</span></a> 。
+<p>我们将以最基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置连接请参考 <span class="xref std std-ref">api_trainer_config_helpers_layers</span> 。
 所有配置都能在 <a class="reference external" href="https://github.com/PaddlePaddle/Paddle">源代码</a> 的 <code class="docutils literal"><span class="pre">demo/quick_start</span></code> 目录下找到。</p>
 <div class="section" id="id11">
 <h3>逻辑回归模型<a class="headerlink" href="#id11" title="永久链接至标题">¶</a></h3>

develop/doc_cn/tutorials/rec/ml_dataset_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/rec/ml_regression_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>
@@ -618,14 +619,14 @@ cp ml-1m/ratings.dat.test .
 并且对用户的特征做同样的操作，也得到一个用户特征。然后我们求这两个特征的余弦相似度。</p>
 <p>在这些网络中，我们用以下的一些:ref:<cite>api_trainer_config</cite> 中的接口。</p>
 <ul class="simple">
-<li>数据层， <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-data-layer"><span class="std std-ref">data_layer</span></a></li>
-<li>全连接层， <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-fc-layer"><span class="std std-ref">fc_layer</span></a></li>
-<li>嵌入层， <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-embedding-layer"><span class="std std-ref">embedding_layer</span></a></li>
-<li>文本投影层， <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-context-projection"><span class="std std-ref">context_projection</span></a></li>
-<li>采样层， <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-pooling-layer"><span class="std std-ref">pooling_layer</span></a></li>
-<li>余弦相似度层， <a class="reference internal" href="../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-cos-sim"><span class="std std-ref">cos_sim</span></a></li>
+<li>数据层， <span class="xref std std-ref">api_trainer_config_helpers_layers_data_layer</span></li>
+<li>全连接层， <span class="xref std std-ref">api_trainer_config_helpers_layers_fc_layer</span></li>
+<li>嵌入层， <span class="xref std std-ref">api_trainer_config_helpers_layers_embedding_layer</span></li>
+<li>文本投影层， <span class="xref std std-ref">api_trainer_config_helpers_layers_context_projection</span></li>
+<li>采样层， <span class="xref std std-ref">api_trainer_config_helpers_layers_pooling_layer</span></li>
+<li>余弦相似度层， <span class="xref std std-ref">api_trainer_config_helpers_layers_cos_sim</span></li>
 <li>文本卷积采样层， <a class="reference internal" href="../../api/v2/config/networks.html#api-trainer-config-helpers-network-text-conv-pool"><span class="std std-ref">text_conv_pool</span></a></li>
-<li>声明Python数据源， <a class="reference internal" href="../../api/v1/trainer_config_helpers/data_sources.html#api-trainer-config-helpers-data-sources"><span class="std std-ref">DataSources</span></a></li>
+<li>声明Python数据源， <span class="xref std std-ref">api_trainer_config_helpers_data_sources</span></li>
 </ul>
 </div>
 <div class="section" id="id9">

develop/doc_cn/tutorials/semantic_role_labeling/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/sentiment_analysis/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>

develop/doc_cn/tutorials/text_generation/index_cn.html

@@ -142,6 +142,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>