From f06f013e9f990cb62683fe1c601b131336aec454 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Wed, 31 Jan 2018 09:32:46 +0000 Subject: [PATCH] Deploy to GitHub Pages: 43262608442a7c3f11422d7a87c0be68ac3293d8 --- .../_sources/api/v2/fluid/data_feeder.rst.txt | 13 +- .../_sources/api/v2/fluid/evaluator.rst.txt | 28 +- .../_sources/api/v2/fluid/executor.rst.txt | 33 +- .../_sources/api/v2/fluid/initializer.rst.txt | 55 +- develop/doc/_sources/api/v2/fluid/io.rst.txt | 61 +- .../doc/_sources/api/v2/fluid/layers.rst.txt | 653 ++- .../doc/_sources/api/v2/fluid/nets.rst.txt | 22 +- .../_sources/api/v2/fluid/optimizer.rst.txt | 65 +- .../_sources/api/v2/fluid/param_attr.rst.txt | 22 +- .../_sources/api/v2/fluid/profiler.rst.txt | 25 +- .../_sources/api/v2/fluid/regularizer.rst.txt | 32 +- develop/doc/api/index_en.html | 22 +- develop/doc/api/v2/config/activation.html | 22 +- develop/doc/api/v2/config/attr.html | 22 +- develop/doc/api/v2/config/evaluators.html | 22 +- 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a/develop/doc/_sources/api/v2/fluid/data_feeder.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/data_feeder.rst.txt @@ -1,9 +1,14 @@ +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + =========== -DataFeeder +data_feeder =========== DataFeeder ------------ -.. automodule:: paddle.v2.fluid.data_feeder - :members: DataFeeder +---------- + +.. autoclass:: paddle.v2.fluid.data_feeder.DataFeeder + :members: :noindex: + diff --git a/develop/doc/_sources/api/v2/fluid/evaluator.rst.txt b/develop/doc/_sources/api/v2/fluid/evaluator.rst.txt index a23f3301d0..00dcecfd62 100644 --- a/develop/doc/_sources/api/v2/fluid/evaluator.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/evaluator.rst.txt @@ -1,9 +1,21 @@ -=========== -Evaluator -=========== - -Evaluator ------------ -.. automodule:: paddle.v2.fluid.evaluator - :members: Evaluator +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +========= +evaluator +========= + +Accuracy +-------- + +.. autoclass:: paddle.v2.fluid.evaluator.Accuracy + :members: :noindex: + +ChunkEvaluator +-------------- + +.. autoclass:: paddle.v2.fluid.evaluator.ChunkEvaluator + :members: + :noindex: + diff --git a/develop/doc/_sources/api/v2/fluid/executor.rst.txt b/develop/doc/_sources/api/v2/fluid/executor.rst.txt index 3a283538c1..a028f6283f 100644 --- a/develop/doc/_sources/api/v2/fluid/executor.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/executor.rst.txt @@ -1,9 +1,32 @@ -=========== -Executor -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +======== +executor +======== Executor +-------- + +.. autoclass:: paddle.v2.fluid.executor.Executor + :members: + :noindex: + +global_scope +------------ + +.. autofunction:: paddle.v2.fluid.executor.global_scope + :noindex: + +scope_guard ----------- -.. automodule:: paddle.v2.fluid.executor - :members: Executor + +.. autofunction:: paddle.v2.fluid.executor.scope_guard + :noindex: + +switch_scope +------------ + +.. autofunction:: paddle.v2.fluid.executor.switch_scope :noindex: + diff --git a/develop/doc/_sources/api/v2/fluid/initializer.rst.txt b/develop/doc/_sources/api/v2/fluid/initializer.rst.txt index 8f587837e9..c38be033ff 100644 --- a/develop/doc/_sources/api/v2/fluid/initializer.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/initializer.rst.txt @@ -1,50 +1,35 @@ +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + =========== -Initializer +initializer =========== +Constant +-------- - -Initializer ------------ -.. automodule:: paddle.v2.fluid.initializer - :members: Initializer - :noindex: - - - -ConstantInitializer -------------------- -.. automodule:: paddle.v2.fluid.initializer - :members: ConstantInitializer +.. autoclass:: paddle.v2.fluid.initializer.Constant + :members: :noindex: +Uniform +------- - -UniformInitializer ------------------- -.. automodule:: paddle.v2.fluid.initializer - :members: UniformInitializer - :noindex: - - - -NormalInitializer ------------------ -.. automodule:: paddle.v2.fluid.initializer - :members: NormalInitializer +.. autoclass:: paddle.v2.fluid.initializer.Uniform + :members: :noindex: +Normal +------ -XavierInitializer ------------------ -.. automodule:: paddle.v2.fluid.initializer - :members: XavierInitializer +.. autoclass:: paddle.v2.fluid.initializer.Normal + :members: :noindex: +Xavier +------ -MSRAInitializer ---------------- -.. automodule:: paddle.v2.fluid.initializer - :members: MSRAInitializer +.. autoclass:: paddle.v2.fluid.initializer.Xavier + :members: :noindex: diff --git a/develop/doc/_sources/api/v2/fluid/io.rst.txt b/develop/doc/_sources/api/v2/fluid/io.rst.txt index 67f68c4e9e..37c9c273e3 100644 --- a/develop/doc/_sources/api/v2/fluid/io.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/io.rst.txt @@ -1,10 +1,61 @@ -=========== -IO -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +== +io +== +save_vars +--------- -is_parameter +.. autofunction:: paddle.v2.fluid.io.save_vars + :noindex: + +save_params ----------- -.. autofunction:: paddle.v2.fluid.io.is_parameter + +.. autofunction:: paddle.v2.fluid.io.save_params + :noindex: + +save_persistables +----------------- + +.. autofunction:: paddle.v2.fluid.io.save_persistables + :noindex: + +load_vars +--------- + +.. autofunction:: paddle.v2.fluid.io.load_vars + :noindex: + +load_params +----------- + +.. autofunction:: paddle.v2.fluid.io.load_params :noindex: + +load_persistables +----------------- + +.. autofunction:: paddle.v2.fluid.io.load_persistables + :noindex: + +save_inference_model +-------------------- + +.. autofunction:: paddle.v2.fluid.io.save_inference_model + :noindex: + +load_inference_model +-------------------- + +.. autofunction:: paddle.v2.fluid.io.load_inference_model + :noindex: + +get_inference_program +--------------------- + +.. autofunction:: paddle.v2.fluid.io.get_inference_program + :noindex: + diff --git a/develop/doc/_sources/api/v2/fluid/layers.rst.txt b/develop/doc/_sources/api/v2/fluid/layers.rst.txt index 231ec2d4ba..e24613b94b 100644 --- a/develop/doc/_sources/api/v2/fluid/layers.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/layers.rst.txt @@ -1,546 +1,799 @@ -========== -Layers -========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +====== +layers +====== -fc ---- -.. autofunction:: paddle.v2.fluid.layers.fc +control_flow +============ + +split_lod_tensor +---------------- + +.. autofunction:: paddle.v2.fluid.layers.split_lod_tensor :noindex: -embedding ---------- -.. autofunction:: paddle.v2.fluid.layers.embedding +merge_lod_tensor +---------------- + +.. autofunction:: paddle.v2.fluid.layers.merge_lod_tensor :noindex: -dynamic_lstm ------------- -.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm +BlockGuard +---------- + +.. autoclass:: paddle.v2.fluid.layers.BlockGuard + :members: :noindex: -dynamic_lstmp -------------- -.. autofunction:: paddle.v2.fluid.layers.dynamic_lstmp +BlockGuardWithCompletion +------------------------ + +.. autoclass:: paddle.v2.fluid.layers.BlockGuardWithCompletion + :members: :noindex: -dynamic_gru ------------ -.. autofunction:: paddle.v2.fluid.layers.dynamic_gru +StaticRNNMemoryLink +------------------- + +.. autoclass:: paddle.v2.fluid.layers.StaticRNNMemoryLink + :members: :noindex: -data ----- -.. autofunction:: paddle.v2.fluid.layers.data +WhileGuard +---------- + +.. autoclass:: paddle.v2.fluid.layers.WhileGuard + :members: :noindex: -mean ----- -.. autofunction:: paddle.v2.fluid.layers.mean +While +----- + +.. autoclass:: paddle.v2.fluid.layers.While + :members: :noindex: -mul ---- -.. autofunction:: paddle.v2.fluid.layers.mul +lod_rank_table +-------------- + +.. autofunction:: paddle.v2.fluid.layers.lod_rank_table :noindex: -elementwise_add ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_add +max_sequence_len +---------------- + +.. autofunction:: paddle.v2.fluid.layers.max_sequence_len :noindex: -elementwise_sub ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_sub +topk +---- + +.. autofunction:: paddle.v2.fluid.layers.topk :noindex: -elementwise_mul ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_mul +lod_tensor_to_array +------------------- + +.. autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array :noindex: -elementwise_div ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_div +array_to_lod_tensor +------------------- + +.. autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor :noindex: +increment +--------- -dropout -------- -.. autofunction:: paddle.v2.fluid.layers.dropout +.. autofunction:: paddle.v2.fluid.layers.increment :noindex: +array_write +----------- -reshape --------- -.. autofunction:: paddle.v2.fluid.layers.reshape +.. autofunction:: paddle.v2.fluid.layers.array_write :noindex: +create_array +------------ -sigmoid +.. autofunction:: paddle.v2.fluid.layers.create_array + :noindex: + +less_than --------- -.. autofunction:: paddle.v2.fluid.layers.sigmoid + +.. autofunction:: paddle.v2.fluid.layers.less_than :noindex: +array_read +---------- -scale ---------- -.. autofunction:: paddle.v2.fluid.layers.scale +.. autofunction:: paddle.v2.fluid.layers.array_read + :noindex: + +shrink_memory +------------- + +.. autofunction:: paddle.v2.fluid.layers.shrink_memory :noindex: +array_length +------------ -transpose +.. autofunction:: paddle.v2.fluid.layers.array_length + :noindex: + +IfElse +------ + +.. autoclass:: paddle.v2.fluid.layers.IfElse + :members: + :noindex: + +DynamicRNN +---------- + +.. autoclass:: paddle.v2.fluid.layers.DynamicRNN + :members: + :noindex: + +ConditionalBlock +---------------- + +.. autoclass:: paddle.v2.fluid.layers.ConditionalBlock + :members: + :noindex: + +StaticRNN --------- -.. autofunction:: paddle.v2.fluid.layers.transpose + +.. autoclass:: paddle.v2.fluid.layers.StaticRNN + :members: :noindex: +reorder_lod_tensor_by_rank +-------------------------- -sigmoid_cross_entropy_with_logits ---------------------------------- -.. autofunction:: paddle.v2.fluid.layers.esigmoid_cross_entropy_with_logits +.. autofunction:: paddle.v2.fluid.layers.reorder_lod_tensor_by_rank :noindex: +ParallelDo +---------- -cast +.. autoclass:: paddle.v2.fluid.layers.ParallelDo + :members: + :noindex: + +Print +----- + +.. autofunction:: paddle.v2.fluid.layers.Print + :noindex: + +device +====== + +get_places +---------- + +.. autofunction:: paddle.v2.fluid.layers.get_places + :noindex: + +io +== + +data ---- -.. autofunction:: paddle.v2.fluid.layers.cast + +.. autofunction:: paddle.v2.fluid.layers.data :noindex: +BlockGuardServ +-------------- -concat -------- -.. autofunction:: paddle.v2.fluid.layers.concat +.. autoclass:: paddle.v2.fluid.layers.BlockGuardServ + :members: :noindex: +ListenAndServ +------------- -sums +.. autoclass:: paddle.v2.fluid.layers.ListenAndServ + :members: + :noindex: + +Send ---- -.. autofunction:: paddle.v2.fluid.layers.sums + +.. autofunction:: paddle.v2.fluid.layers.Send :noindex: +nn +== -linear_chain_crf ----------------- -.. autofunction:: paddle.v2.fluid.layers.linear_chain_crf +fc +-- + +.. autofunction:: paddle.v2.fluid.layers.fc :noindex: +embedding +--------- -assign -------- .. autofunction:: paddle.v2.fluid.layers.embedding :noindex: +dynamic_lstm +------------ -split_lod_tensor ----------------- -.. autofunction:: paddle.v2.fluid.layers.split_lod_tensor +.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm :noindex: +dynamic_lstmp +------------- -merge_lod_tensor +.. autofunction:: paddle.v2.fluid.layers.dynamic_lstmp + :noindex: + +dynamic_gru +----------- + +.. autofunction:: paddle.v2.fluid.layers.dynamic_gru + :noindex: + +gru_unit +-------- + +.. autofunction:: paddle.v2.fluid.layers.gru_unit + :noindex: + +linear_chain_crf ---------------- -.. autofunction:: paddle.v2.fluid.layers.merge_lod_tensor + +.. autofunction:: paddle.v2.fluid.layers.linear_chain_crf + :noindex: + +crf_decoding +------------ + +.. autofunction:: paddle.v2.fluid.layers.crf_decoding :noindex: cos_sim --------- +------- + .. autofunction:: paddle.v2.fluid.layers.cos_sim :noindex: - cross_entropy ------------- + .. autofunction:: paddle.v2.fluid.layers.cross_entropy :noindex: - - square_error_cost ----------------- + .. autofunction:: paddle.v2.fluid.layers.square_error_cost :noindex: - accuracy ---------- +-------- + .. autofunction:: paddle.v2.fluid.layers.accuracy :noindex: +chunk_eval +---------- + +.. autofunction:: paddle.v2.fluid.layers.chunk_eval + :noindex: sequence_conv ------------- + .. autofunction:: paddle.v2.fluid.layers.sequence_conv :noindex: - conv2d ------ + .. autofunction:: paddle.v2.fluid.layers.conv2d :noindex: - sequence_pool ------------- + .. autofunction:: paddle.v2.fluid.layers.sequence_pool :noindex: +pool2d +------ -sequence_first_step -------------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_first_step +.. autofunction:: paddle.v2.fluid.layers.pool2d :noindex: +batch_norm +---------- + +.. autofunction:: paddle.v2.fluid.layers.batch_norm + :noindex: -sequence_last_step +beam_search_decode ------------------ -.. autofunction:: paddle.v2.fluid.layers.sequence_last_step + +.. autofunction:: paddle.v2.fluid.layers.beam_search_decode :noindex: +conv2d_transpose +---------------- -pool2d ------- -.. autofunction:: paddle.v2.fluid.layers.pool2d +.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose :noindex: +sequence_expand +--------------- -batch_norm +.. autofunction:: paddle.v2.fluid.layers.sequence_expand + :noindex: + +lstm_unit +--------- + +.. autofunction:: paddle.v2.fluid.layers.lstm_unit + :noindex: + +reduce_sum ---------- -.. autofunction:: paddle.v2.fluid.layers.batch_norm + +.. autofunction:: paddle.v2.fluid.layers.reduce_sum + :noindex: + +reduce_mean +----------- + +.. autofunction:: paddle.v2.fluid.layers.reduce_mean :noindex: +reduce_max +---------- + +.. autofunction:: paddle.v2.fluid.layers.reduce_max + :noindex: -beam_search_decode +reduce_min +---------- + +.. autofunction:: paddle.v2.fluid.layers.reduce_min + :noindex: + +sequence_first_step +------------------- + +.. autofunction:: paddle.v2.fluid.layers.sequence_first_step + :noindex: + +sequence_last_step ------------------ -.. autofunction:: paddle.v2.fluid.layers.beam_search_decode + +.. autofunction:: paddle.v2.fluid.layers.sequence_last_step + :noindex: + +dropout +------- + +.. autofunction:: paddle.v2.fluid.layers.dropout :noindex: +split +----- -lod_rank_table --------------- -.. autofunction:: paddle.v2.fluid.layers.lod_rank_table +.. autofunction:: paddle.v2.fluid.layers.split :noindex: +ctc_greedy_decoder +------------------ -max_sequence_len ----------------- -.. autofunction:: paddle.v2.fluid.layers.max_sequence_len +.. autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder :noindex: +edit_distance +------------- -topk ------ -.. autofunction:: paddle.v2.fluid.layers.topk +.. autofunction:: paddle.v2.fluid.layers.edit_distance :noindex: +l2_normalize +------------ -lod_tensor_to_array -------------------- -.. autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array +.. autofunction:: paddle.v2.fluid.layers.l2_normalize :noindex: +matmul +------ - -array_to_lod_tensor -------------------- -.. autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor +.. autofunction:: paddle.v2.fluid.layers.matmul :noindex: +warpctc +------- +.. autofunction:: paddle.v2.fluid.layers.warpctc + :noindex: +sequence_reshape +---------------- -fill_constant -------------- -.. autofunction:: paddle.v2.fluid.layers.fill_constant +.. autofunction:: paddle.v2.fluid.layers.sequence_reshape :noindex: +transpose +--------- +.. autofunction:: paddle.v2.fluid.layers.transpose + :noindex: -fill_constant_batch_size_like ------------------------------ -.. autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like +im2sequence +----------- + +.. autofunction:: paddle.v2.fluid.layers.im2sequence :noindex: +nce +--- -ones ----- -.. autofunction:: paddle.v2.fluid.layers.ones +.. autofunction:: paddle.v2.fluid.layers.nce :noindex: +beam_search +----------- -zeros ------ -.. autofunction:: paddle.v2.fluid.layers.zeros +.. autofunction:: paddle.v2.fluid.layers.beam_search :noindex: +row_conv +-------- -increment ---------- -.. autofunction:: paddle.v2.fluid.layers.increment +.. autofunction:: paddle.v2.fluid.layers.row_conv :noindex: +multiplex +--------- -array_write ------------ -.. autofunction:: paddle.v2.fluid.layers.array_write +.. autofunction:: paddle.v2.fluid.layers.multiplex :noindex: +ops +=== +mean +---- -create_array ------------- -.. autofunction:: paddle.v2.fluid.layers.create_array +.. autofunction:: paddle.v2.fluid.layers.mean :noindex: +mul +--- -less_than ---------- -.. autofunction:: paddle.v2.fluid.layers.less_than +.. autofunction:: paddle.v2.fluid.layers.mul :noindex: +reshape +------- -array_read ----------- -.. autofunction:: paddle.v2.fluid.layers.array_read +.. autofunction:: paddle.v2.fluid.layers.reshape :noindex: +scale +----- -shrink_memory --------------- -.. autofunction:: paddle.v2.fluid.layers.shrink_memory +.. autofunction:: paddle.v2.fluid.layers.scale :noindex: +sigmoid_cross_entropy_with_logits +--------------------------------- -array_length -------------- -.. autofunction:: paddle.v2.fluid.layers.array_length +.. autofunction:: paddle.v2.fluid.layers.sigmoid_cross_entropy_with_logits :noindex: +elementwise_add +--------------- -conv2d_transpose ----------------- -.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose +.. autofunction:: paddle.v2.fluid.layers.elementwise_add :noindex: - -sequence_expand +elementwise_div --------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_expand + +.. autofunction:: paddle.v2.fluid.layers.elementwise_div :noindex: +elementwise_sub +--------------- -gru_unit --------- -.. autofunction:: paddle.v2.fluid.layers.gru_unit +.. autofunction:: paddle.v2.fluid.layers.elementwise_sub :noindex: +elementwise_mul +--------------- -lstm_unit ---------- -.. autofunction:: paddle.v2.fluid.layers.lstm_unit +.. autofunction:: paddle.v2.fluid.layers.elementwise_mul :noindex: +elementwise_max +--------------- -sequence_softmax ----------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_softmax +.. autofunction:: paddle.v2.fluid.layers.elementwise_max :noindex: +elementwise_min +--------------- -reduce_sum ----------- -.. autofunction:: paddle.v2.fluid.layers.reduce_sum +.. autofunction:: paddle.v2.fluid.layers.elementwise_min :noindex: +elementwise_pow +--------------- -reduce_mean ------------ -.. autofunction:: paddle.v2.fluid.layers.reduce_mean +.. autofunction:: paddle.v2.fluid.layers.elementwise_pow :noindex: +clip +---- -reduce_max ----------- -.. autofunction:: paddle.v2.fluid.layers.reduce_max +.. autofunction:: paddle.v2.fluid.layers.clip :noindex: +clip_by_norm +------------ -reduce_min ----------- -.. autofunction:: paddle.v2.fluid.layers.reduce_min +.. autofunction:: paddle.v2.fluid.layers.clip_by_norm :noindex: +sequence_softmax +---------------- -split ------ -.. autofunction:: paddle.v2.fluid.layers.split +.. autofunction:: paddle.v2.fluid.layers.sequence_softmax :noindex: +sigmoid +------- -matmul ------- -.. autofunction:: paddle.v2.fluid.layers.matmul +.. autofunction:: paddle.v2.fluid.layers.sigmoid :noindex: logsigmoid ---------- + .. autofunction:: paddle.v2.fluid.layers.logsigmoid :noindex: exp --- + .. autofunction:: paddle.v2.fluid.layers.exp :noindex: relu ---- + .. autofunction:: paddle.v2.fluid.layers.relu :noindex: tanh ---- + .. autofunction:: paddle.v2.fluid.layers.tanh :noindex: tanh_shrink ----------- + .. autofunction:: paddle.v2.fluid.layers.tanh_shrink :noindex: softshrink ---------- + .. autofunction:: paddle.v2.fluid.layers.softshrink :noindex: sqrt ---- + .. autofunction:: paddle.v2.fluid.layers.sqrt :noindex: abs ----- +--- + .. autofunction:: paddle.v2.fluid.layers.abs :noindex: ceil ---- + .. autofunction:: paddle.v2.fluid.layers.ceil :noindex: floor ----- + .. autofunction:: paddle.v2.fluid.layers.floor :noindex: round ----- + .. autofunction:: paddle.v2.fluid.layers.round :noindex: reciprocal ---------- + .. autofunction:: paddle.v2.fluid.layers.reciprocal :noindex: log --- + .. autofunction:: paddle.v2.fluid.layers.log :noindex: square ------ + .. autofunction:: paddle.v2.fluid.layers.square :noindex: softplus -------- + .. autofunction:: paddle.v2.fluid.layers.softplus :noindex: softsign ---------- +-------- + .. autofunction:: paddle.v2.fluid.layers.softsign :noindex: brelu ----- + .. autofunction:: paddle.v2.fluid.layers.brelu :noindex: leaky_relu ---------- + .. autofunction:: paddle.v2.fluid.layers.leaky_relu :noindex: soft_relu --------- + .. autofunction:: paddle.v2.fluid.layers.soft_relu :noindex: elu ----- +--- + .. autofunction:: paddle.v2.fluid.layers.elu :noindex: relu6 ----- + .. autofunction:: paddle.v2.fluid.layers.relu6 :noindex: pow ----- +--- + .. autofunction:: paddle.v2.fluid.layers.pow :noindex: +stanh +----- + +.. autofunction:: paddle.v2.fluid.layers.stanh + :noindex: + hard_shrink ----------- + .. autofunction:: paddle.v2.fluid.layers.hard_shrink :noindex: thresholded_relu ---------------- + .. autofunction:: paddle.v2.fluid.layers.thresholded_relu :noindex: hard_sigmoid -------------- +------------ + .. autofunction:: paddle.v2.fluid.layers.hard_sigmoid :noindex: swish ------- +----- + .. autofunction:: paddle.v2.fluid.layers.swish :noindex: -im2sequence +tensor +====== + +create_tensor +------------- + +.. autofunction:: paddle.v2.fluid.layers.create_tensor + :noindex: + +create_parameter +---------------- + +.. autofunction:: paddle.v2.fluid.layers.create_parameter + :noindex: + +create_global_var +----------------- + +.. autofunction:: paddle.v2.fluid.layers.create_global_var + :noindex: + +cast +---- + +.. autofunction:: paddle.v2.fluid.layers.cast + :noindex: + +concat ------ -.. autofunction:: paddle.v2.fluid.layers.im2sequence + +.. autofunction:: paddle.v2.fluid.layers.concat :noindex: -edit_distance ---------------- -.. autofunction:: paddle.v2.fluid.layers.edit_distance_error +sums +---- + +.. autofunction:: paddle.v2.fluid.layers.sums :noindex: -ctc_greedy_decoder ---------------- -.. autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder +assign +------ + +.. autofunction:: paddle.v2.fluid.layers.assign :noindex: -l2_normalize ------------- -.. autofunction:: paddle.v2.fluid.layers.l2_normalize +fill_constant_batch_size_like +----------------------------- + +.. autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like :noindex: -sequence_reshape ----------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_reshape +fill_constant +------------- + +.. autofunction:: paddle.v2.fluid.layers.fill_constant :noindex: -row_conv --------- -.. autofunction:: paddle.v2.fluid.layers.row_conv +ones +---- + +.. autofunction:: paddle.v2.fluid.layers.ones :noindex: -multiplex ---------- -.. autofunction:: paddle.v2.fluid.layers.multiplex +zeros +----- + +.. autofunction:: paddle.v2.fluid.layers.zeros :noindex: + diff --git a/develop/doc/_sources/api/v2/fluid/nets.rst.txt b/develop/doc/_sources/api/v2/fluid/nets.rst.txt index 500019bc50..015581b766 100644 --- a/develop/doc/_sources/api/v2/fluid/nets.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/nets.rst.txt @@ -1,33 +1,31 @@ -=========== -Nets -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +==== +nets +==== simple_img_conv_pool -------------------- -.. autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool - :noindex: - -img_conv_group ---------------- -.. autofunction:: paddle.v2.fluid.nets.img_conv_group +.. autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool :noindex: - sequence_conv_pool ------------------ + .. autofunction:: paddle.v2.fluid.nets.sequence_conv_pool :noindex: - glu --- + .. autofunction:: paddle.v2.fluid.nets.glu :noindex: - scaled_dot_product_attention ---------------------------- + .. autofunction:: paddle.v2.fluid.nets.scaled_dot_product_attention :noindex: diff --git a/develop/doc/_sources/api/v2/fluid/optimizer.rst.txt b/develop/doc/_sources/api/v2/fluid/optimizer.rst.txt index 19b4940f08..1691ebb9a7 100644 --- a/develop/doc/_sources/api/v2/fluid/optimizer.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/optimizer.rst.txt @@ -1,54 +1,49 @@ -=========== -Optimizer -=========== - -Optimizer ------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: Optimizer - :noindex: +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +========= +optimizer +========= -SGDOptimizer ------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: SGDOptimizer - :noindex: +SGD +--- +.. autoclass:: paddle.v2.fluid.optimizer.SGD + :members: + :noindex: +Momentum +-------- -MomentumOptimizer ------------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: MomentumOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Momentum + :members: :noindex: +Adagrad +------- - -AdagradOptimizer ----------------- -.. automodule:: paddle.v2.fluid.optimizer - :members: AdagradOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Adagrad + :members: :noindex: +Adam +---- -AdamOptimizer -------------- -.. automodule:: paddle.v2.fluid.optimizer - :members: AdamOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Adam + :members: :noindex: +Adamax +------ -AdamaxOptimizer ------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: AdamaxOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Adamax + :members: :noindex: +DecayedAdagrad +-------------- -DecayedAdagradOptimizer ------------------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: DecayedAdagradOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.DecayedAdagrad + :members: :noindex: diff --git a/develop/doc/_sources/api/v2/fluid/param_attr.rst.txt b/develop/doc/_sources/api/v2/fluid/param_attr.rst.txt index ca0c8af9e8..8083d0d858 100644 --- a/develop/doc/_sources/api/v2/fluid/param_attr.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/param_attr.rst.txt @@ -1,11 +1,21 @@ -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +========== +param_attr +========== + ParamAttr -=========== +--------- +.. autoclass:: paddle.v2.fluid.param_attr.ParamAttr + :members: + :noindex: +WeightNormParamAttr +------------------- -ParamAttr ------------ -.. automodule:: paddle.v2.fluid.param_attr - :members: ParamAttr +.. autoclass:: paddle.v2.fluid.param_attr.WeightNormParamAttr + :members: :noindex: + diff --git a/develop/doc/_sources/api/v2/fluid/profiler.rst.txt b/develop/doc/_sources/api/v2/fluid/profiler.rst.txt index 7d4042d1f4..4a1ff7cb69 100644 --- a/develop/doc/_sources/api/v2/fluid/profiler.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/profiler.rst.txt @@ -1,10 +1,25 @@ -=========== -Profiler -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +======== +profiler +======== +cuda_profiler +------------- -Profiler ------------ .. autofunction:: paddle.v2.fluid.profiler.cuda_profiler :noindex: + +reset_profiler +-------------- + +.. autofunction:: paddle.v2.fluid.profiler.reset_profiler + :noindex: + +profiler +-------- + +.. autofunction:: paddle.v2.fluid.profiler.profiler + :noindex: + diff --git a/develop/doc/_sources/api/v2/fluid/regularizer.rst.txt b/develop/doc/_sources/api/v2/fluid/regularizer.rst.txt index 868e225ed3..2c17d15599 100644 --- a/develop/doc/_sources/api/v2/fluid/regularizer.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/regularizer.rst.txt @@ -1,25 +1,27 @@ +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + =========== -Regularizer +regularizer =========== -WeightDecayRegularizer ----------------------- -.. automodule:: paddle.v2.fluid.regularizer - :members: WeightDecayRegularizer - :noindex: - +append_regularization_ops +------------------------- -L2DecayRegularizer ------------------- -.. automodule:: paddle.v2.fluid.regularizer - :members: L2DecayRegularizer +.. autofunction:: paddle.v2.fluid.regularizer.append_regularization_ops :noindex: +L1Decay +------- +.. autoclass:: paddle.v2.fluid.regularizer.L1Decay + :members: + :noindex: -L1DecayRegularizer -------------------- -.. automodule:: paddle.v2.fluid.regularizer - :members: L1DecayRegularizer +L2Decay +------- +.. autoclass:: paddle.v2.fluid.regularizer.L2Decay + :members: + :noindex: diff --git a/develop/doc/api/index_en.html b/develop/doc/api/index_en.html index eaf4d803b3..64d35c6027 100644 --- a/develop/doc/api/index_en.html +++ b/develop/doc/api/index_en.html @@ -161,17 +161,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/config/activation.html b/develop/doc/api/v2/config/activation.html index d5a7c774a3..34901501dd 100644 --- a/develop/doc/api/v2/config/activation.html +++ b/develop/doc/api/v2/config/activation.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/config/attr.html b/develop/doc/api/v2/config/attr.html index 9411485212..c96d29213e 100644 --- a/develop/doc/api/v2/config/attr.html +++ b/develop/doc/api/v2/config/attr.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/config/evaluators.html b/develop/doc/api/v2/config/evaluators.html index 8d94cfae13..75a7662a55 100644 --- a/develop/doc/api/v2/config/evaluators.html +++ b/develop/doc/api/v2/config/evaluators.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/config/layer.html b/develop/doc/api/v2/config/layer.html index 59bb0e493e..46653b63e2 100644 --- a/develop/doc/api/v2/config/layer.html +++ b/develop/doc/api/v2/config/layer.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/config/networks.html b/develop/doc/api/v2/config/networks.html index 8de7433b86..d7d5f12260 100644 --- a/develop/doc/api/v2/config/networks.html +++ b/develop/doc/api/v2/config/networks.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/config/optimizer.html b/develop/doc/api/v2/config/optimizer.html index 54f0a51d06..8e006cb0d3 100644 --- a/develop/doc/api/v2/config/optimizer.html +++ b/develop/doc/api/v2/config/optimizer.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/config/pooling.html b/develop/doc/api/v2/config/pooling.html index a4ebe1918a..6896032e94 100644 --- a/develop/doc/api/v2/config/pooling.html +++ b/develop/doc/api/v2/config/pooling.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/data.html b/develop/doc/api/v2/data.html index 22bf3adccc..cd6e26d44a 100644 --- a/develop/doc/api/v2/data.html +++ b/develop/doc/api/v2/data.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/data/data_reader.html b/develop/doc/api/v2/data/data_reader.html index 843fd04e5a..d6db947b15 100644 --- a/develop/doc/api/v2/data/data_reader.html +++ b/develop/doc/api/v2/data/data_reader.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/data/dataset.html b/develop/doc/api/v2/data/dataset.html index 00774e9712..ae086f87bf 100644 --- a/develop/doc/api/v2/data/dataset.html +++ b/develop/doc/api/v2/data/dataset.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/data/image.html b/develop/doc/api/v2/data/image.html index b11964f742..4cea42a092 100644 --- a/develop/doc/api/v2/data/image.html +++ b/develop/doc/api/v2/data/image.html @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc/api/v2/fluid.html b/develop/doc/api/v2/fluid.html index db9048fe9a..b22facab89 100644 --- a/develop/doc/api/v2/fluid.html +++ b/develop/doc/api/v2/fluid.html @@ -34,7 +34,7 @@ - + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -218,17 +218,17 @@

    Fluid

    @@ -240,7 +240,7 @@ @@ -216,10 +216,15 @@
    -
    -

    DataFeeder

    -
    -

    DataFeeder

    +
    +

    data_feeder

    +
    +

    DataFeeder

    +
    +
    +class paddle.v2.fluid.data_feeder.DataFeeder(feed_list, place, program=None)
    +
    +
    @@ -230,10 +235,10 @@ diff --git a/develop/doc/api/v2/fluid/evaluator.html b/develop/doc/api/v2/fluid/evaluator.html index a44ce3e959..a5230f3ccf 100644 --- a/develop/doc/api/v2/fluid/evaluator.html +++ b/develop/doc/api/v2/fluid/evaluator.html @@ -8,7 +8,7 @@ - Evaluator — PaddlePaddle documentation + evaluator — PaddlePaddle documentation @@ -34,8 +34,8 @@ - - + + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • Evaluator
    • +
  • evaluator
    • @@ -217,76 +217,24 @@
    -

    Evaluator

    -
    -

    Evaluator

    +

    evaluator

    +
    +

    Accuracy

    -class paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)
    -

    Base Class for all evaluators

    - --- - - - -
    Parameters:
      -
    • name (str) – The name of evaluator. such as, “accuracy”. Used for generate -temporary variable name.
      • -
    • main_program (Program, optional) – The evaluator should be added to this -main_program. Default default_main_program()
      • -
    • startup_program (Program, optional) – The parameter should be added to this -startup_program. Default default_startup_program()
      • -
    -
    -
    -
    -states
    -

    list – The list of state variables. states will be reset to zero -when reset is invoked.

    -
    - -
    -
    -metrics
    -

    list – The list of metrics variables. They will be calculate -every mini-batch

    -
    - -
    -
    -reset(executor, reset_program=None)
    -

    reset metric states at the begin of each pass/user specified batch

    -
    - -
    -
    -eval(executor, eval_program=None)
    -

    Evaluate the statistics merged by multiple mini-batches.

    +class paddle.v2.fluid.evaluator.Accuracy(input, label, k=1, **kwargs) +

    Average Accuracy for multiple mini-batches.

    -
    +
    +
    +

    ChunkEvaluator

    +
    -create_state(suffix, dtype, shape)
    -

    Create state variable.

    -

    NOTE: It is not a public API.

    - --- - - - -
    Parameters:
      -
    • suffix (str) – the state suffix.
      • -
    • dtype (str|core.DataType) – the state data type
      • -
    • shape (tuple|list) – the shape of state
      • -
    -
    -

    Returns: State variable

    -
    - +class paddle.v2.fluid.evaluator.ChunkEvaluator(input, label, chunk_scheme, num_chunk_types, excluded_chunk_types=None) +

    Accumulate counter numbers output by chunk_eval from mini-batches and +compute the precision recall and F1-score using the accumulated counter +numbers.

    @@ -299,10 +247,10 @@ every mini-batch

    diff --git a/develop/doc/api/v2/fluid/executor.html b/develop/doc/api/v2/fluid/executor.html index aa07c2f1e0..a81a5d9ab2 100644 --- a/develop/doc/api/v2/fluid/executor.html +++ b/develop/doc/api/v2/fluid/executor.html @@ -8,7 +8,7 @@ - Executor — PaddlePaddle documentation + executor — PaddlePaddle documentation @@ -34,8 +34,8 @@ - - + + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • Executor
    • +
  • executor
    • @@ -217,9 +217,38 @@
    -

    Executor

    +

    executor

    Executor

    +
    +
    +class paddle.v2.fluid.executor.Executor(places)
    +
    + +
    +
    +

    global_scope

    +
    +
    +paddle.v2.fluid.executor.global_scope()
    +
    + +
    +
    +

    scope_guard

    +
    +
    +paddle.v2.fluid.executor.scope_guard(*args, **kwds)
    +
    + +
    +
    +

    switch_scope

    +
    +
    +paddle.v2.fluid.executor.switch_scope(scope)
    +
    +
    @@ -230,10 +259,10 @@ diff --git a/develop/doc/api/v2/fluid/initializer.html b/develop/doc/api/v2/fluid/initializer.html index d248f22940..c8cf498be9 100644 --- a/develop/doc/api/v2/fluid/initializer.html +++ b/develop/doc/api/v2/fluid/initializer.html @@ -8,7 +8,7 @@ - Initializer — PaddlePaddle documentation + initializer — PaddlePaddle documentation @@ -34,8 +34,8 @@ - - + + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • Initializer
    • +
  • initializer
    • @@ -217,91 +217,40 @@
    -

    Initializer

    -
    -

    Initializer

    -
    +

    initializer

    +
    +

    Constant

    +
    -class paddle.v2.fluid.initializer.Initializer
    -

    Base class for variable initializers

    -

    Defines the common interface of variable initializers. -They add operations to the init program that are used -to initialize variables. Users should not use this class -directly, but need to use one of its implementations.

    +paddle.v2.fluid.initializer.Constant +

    alias of ConstantInitializer

    -
    -

    ConstantInitializer

    -
    +
    +

    Uniform

    +
    -class paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)
    -

    Implements the constant initializer

    +paddle.v2.fluid.initializer.Uniform +

    alias of UniformInitializer

    -
    -

    UniformInitializer

    -
    +
    +

    Normal

    +
    -class paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)
    -

    Implements the random uniform distribution initializer

    +paddle.v2.fluid.initializer.Normal +

    alias of NormalInitializer

    -
    -

    NormalInitializer

    -
    +
    +

    Xavier

    +
    -class paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)
    -

    Implements the random Normal(Gaussian) distribution initializer

    -
    - -
    -
    -

    XavierInitializer

    -
    -
    -class paddle.v2.fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0)
    -

    Implements the Xavier initializer

    -

    This class implements the Xavier weight initializer from the paper -Understanding the difficulty of training deep feedforward neural -networks[1] by Xavier Glorot and Yoshua Bengio.

    -

    This initializer is designed to keep the scale of the gradients -approximately same in all the layers. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)). -In case of Normal distribution, the mean is 0 and the standard deviation -is sqrt(2/ (fan_in + fan_out)).

    -

    References

    -
    -
    [1] Understanding the difficulty of training deep feedforward neural
    -
    networks. International conference on artificial intelligence and -statistics. -(http://proceedings.mlr.press/v9/glorot10a.html)
    -
    -
    - -
    -
    -

    MSRAInitializer

    -
    -
    -class paddle.v2.fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0)
    -

    Implements the MSRA initializer a.k.a. Kaiming Initializer

    -

    This class implements the weight initialization from the paper -Delving Deep into Rectifiers: Surpassing Human-Level Performance on -ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren -and Jian Sun. This is a robust initialization method that particularly -considers the rectifier nonlinearities. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal -distribution, the mean is 0 and the standard deviation -is sqrt(2/ fan_in).

    -

    References

    -
    -
    [1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
    -
    on ImageNet Classification -(https://arxiv.org/abs/1502.01852)
    -
    +paddle.v2.fluid.initializer.Xavier +

    alias of XavierInitializer

    @@ -314,10 +263,10 @@ is sqrt(2/ fan_in).

    diff --git a/develop/doc/api/v2/fluid/io.html b/develop/doc/api/v2/fluid/io.html index 35c0fe2639..a133eeccf8 100644 --- a/develop/doc/api/v2/fluid/io.html +++ b/develop/doc/api/v2/fluid/io.html @@ -8,7 +8,7 @@ - IO — PaddlePaddle documentation + io — PaddlePaddle documentation @@ -35,7 +35,7 @@ - + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • IO
    • +
  • io
    • @@ -217,26 +217,162 @@
    -

    IO

    -
    -

    is_parameter

    +

    io

    +
    +

    save_vars

    -paddle.v2.fluid.io.is_parameter(var)
    -

    Check whether the variable is a Parameter.

    -

    This function checks whether the input variable is a Parameter.

    +paddle.v2.fluid.io.save_vars(executor, dirname, main_program=None, vars=None, predicate=None) +

    Save variables to directory by executor.

    - + - + +
    Parameters:var – The input variable.
    Parameters:
      +
    • executor – executor that save variable
      • +
    • dirname – directory path
      • +
    • main_program – program. If vars is None, then filter all variables in this
      • +
    +
    Returns:boolean result whether the variable is a Parameter.
    +

    program which fit predicate. Default default_main_program. +:param predicate: The Predicate describes a callable that returns a variable +as a bool. If it returns true, the variables will be saved. +:param vars: variables need to be saved. If specify vars, program & predicate +will be ignored +:return: None

    +
    + +
    +
    +

    save_params

    +
    +
    +paddle.v2.fluid.io.save_params(executor, dirname, main_program=None)
    +

    Save all parameters to directory with executor.

    +
    + +
    +
    +

    save_persistables

    +
    +
    +paddle.v2.fluid.io.save_persistables(executor, dirname, main_program=None)
    +

    Save all persistables to directory with executor.

    +
    + +
    +
    +

    load_vars

    +
    +
    +paddle.v2.fluid.io.load_vars(executor, dirname, main_program=None, vars=None, predicate=None)
    +

    Load variables from directory by executor.

    + +++ +
    Parameters:
      +
    • executor – executor that save variable
      • +
    • dirname – directory path
      • +
    • main_program – program. If vars is None, then filter all variables in this
      • +
    +
    +

    program which fit predicate. Default default_main_program(). +:param predicate: The Predicate describes a callable that returns a variable +as a bool. If it returns true, the variables will be loaded. +:param vars: variables need to be loaded. If specify vars, program & +predicate will be ignored +:return: None

    +
    + +
    +
    +

    load_params

    +
    +
    +paddle.v2.fluid.io.load_params(executor, dirname, main_program=None)
    +

    load all parameters from directory by executor.

    +
    + +
    +
    +

    load_persistables

    +
    +
    +paddle.v2.fluid.io.load_persistables(executor, dirname, main_program=None)
    +

    load all persistables from directory by executor.

    +
    +
    +

    save_inference_model

    +
    +
    +paddle.v2.fluid.io.save_inference_model(dirname, feeded_var_names, target_vars, executor, main_program=None)
    +

    Build a model especially for inference, +and save it to directory by the executor.

    + +++ + + + + + +
    Parameters:
      +
    • dirname – directory path
      • +
    • feeded_var_names – Names of variables that need to be feeded data during inference
      • +
    • target_vars – Variables from which we can get inference results.
      • +
    • executor – executor that save inference model
      • +
    • main_program – original program, which will be pruned to build the inference model. +Default default_main_program().
      • +
    +
    Returns:

    None

    +
    +
    + +
    +
    +

    load_inference_model

    +
    +
    +paddle.v2.fluid.io.load_inference_model(dirname, executor)
    +

    Load inference model from a directory

    + +++ + + + + + +
    Parameters:
      +
    • dirname – directory path
      • +
    • executor – executor that load inference model
      • +
    +
    Returns:

    [program, feed_target_names, fetch_targets] +program: program especially for inference. +feed_target_names: Names of variables that need to feed data +fetch_targets: Variables from which we can get inference results.

    +
    +
    + +
    +
    +

    get_inference_program

    +
    +
    +paddle.v2.fluid.io.get_inference_program(target_vars, main_program=None)
    +
    +
    @@ -250,7 +386,7 @@ Next - Previous + Previous
    diff --git a/develop/doc/api/v2/fluid/layers.html b/develop/doc/api/v2/fluid/layers.html index eb2cdeea51..6e8d381e5b 100644 --- a/develop/doc/api/v2/fluid/layers.html +++ b/develop/doc/api/v2/fluid/layers.html @@ -8,7 +8,7 @@ - Layers — PaddlePaddle documentation + layers — PaddlePaddle documentation @@ -34,7 +34,7 @@ - + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • Layers
    • +
  • layers
    • @@ -217,127 +217,79 @@
    -

    Layers

    -
    -

    fc

    +

    layers

    +
    +

    control_flow

    +
    +

    split_lod_tensor

    -paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None)
    -

    Fully Connected Layer

    -

    The fully connected layer can take multiple tensors as its inputs. It -creates a variable (one for each input tensor) called weights for each -input tensor, which represents a fully connected weight matrix from -each input unit to each output unit. The fully connected layer -multiplies each input tensor with its coresponding weight to produce -an output Tensor. If multiple input tensors are given, the results of -multiple multiplications will be sumed up. If bias_attr is not None, -a biases variable will be created and added to the output. Finally, -if activation is not None, it will be applied to the output as well.

    -

    This process can be formulated as follows:

    -
    -\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]
    -

    In the above equation:

    -
      -
    • \(N\): Number of the input.
      • -
    • \(X_i\): The input tensor.
      • -
    • \(W\): The weights created by this layer.
      • -
    • \(b\): The bias parameter created by this layer (if needed).
      • -
    • \(Act\): The activation funtion.
      • -
    • \(Out\): The output tensor.
      • -
    +paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0) +

    split_lod_tensor

    +

    This function takes in an input that contains the complete lod information, +and takes in a mask which is used to mask certain parts of the input. +The output is the true branch and the false branch with the mask applied to +the input at a certain level in the tensor.

    - - - -
    Parameters:
      -
    • input (Variable|list) – The input tensor(s) to the fully connected layer.
      • -
    • size (int) – The number of output units in the fully connected layer.
      • -
    • num_flatten_dims (int) – The fc layer can accept an input tensor with more -than two dimensions. If this happens, the -multidimensional tensor will first be flattened -into a 2-dimensional matrix. The parameter -num_flatten_dims determines how the input tensor -is flattened: the first num_flatten_dims -(inclusive, index starts from 1) dimensions will -be flatten to form the first dimension of the -final matrix (height of the matrix), and the rest -rank(X) - num_flatten_dims dimensions are -flattened to form the second dimension of the -final matrix (width of the matrix). For example, -suppose X is a 6-dimensional tensor with a shape -[2, 3, 4, 5, 6], and num_flatten_dims = 3. Then, -the flattened matrix will have a shape -[2 x 3 x 4, 5 x 6] = [24, 30]. By default, -num_flatten_dims is set to 1.
      • -
    • param_attr (ParamAttr|list) – The parameter attribute for learnable -parameters/weights of the fully connected -layer.
      • -
    • param_initializer (ParamAttr|list) – The initializer used for the -weight/parameter. If set None, -XavierInitializer() will be used.
      • -
    • bias_attr (ParamAttr|list) – The parameter attribute for the bias parameter -for this layer. If set None, no bias will be -added to the output units.
      • -
    • bias_initializer (ParamAttr|list) – The initializer used for the bias. -If set None, then ConstantInitializer() -will be used.
      • -
    • act (str) – Activation to be applied to the output of the fully connected -layer.
      • -
    • name (str) – Name/alias of the fully connected layer.
      • +
    • input (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
      • +
    • mask (list) – A bool column vector which masks the input.
      • +
    • level (int) – The specific lod level to rank.
    Returns:

    The output tensor variable.

    -
    Return type:

    Variable

    +
    Returns:

    The true branch of tensor as per the mask applied to input. +Variable: The false branch of tensor as per the mask applied to input.
    Raises:

    ValueError – If rank of the input tensor is less than 2.

    +
    Return type:

    Variable

    Examples

    -
    data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
-fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+
    x = layers.data(name='x', shape=[1])
+x.persistable = True
+
+y = layers.data(name='y', shape=[1])
+y.persistable = True
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
    -
    -

    embedding

    +
    +

    merge_lod_tensor

    -paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, padding_idx=None, param_attr=None, dtype='float32')
    -

    Embedding Layer

    -

    This layer is used to lookup embeddings of IDs, provided by input, in -a lookup table. The result of this lookup is the embedding of each ID in the -input.

    -

    All the input variables are passed in as local variables to the LayerHelper -constructor.

    +paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0) +

    merge_lod_tensor

    +

    This function takes in an input \(x\), the True branch, the False +branch and a binary \(mask\). Using this information, this function +merges the True and False branches of the tensor into a single Output +at a certain lod level indiacted by \(level\).

    - + + + + +
    Parameters:
      -
    • input (Variable) – The tensor variable containing the IDs.
      • -
    • size (tuple|list) – The shape of the look up table parameter. It should -have two elements which indicate the size of the dictionary of -embeddings and the size of each embedding vector respectively.
      • -
    • is_sparse (bool) – The flag indicating whether to use sparse update.
      • -
    • padding_idx (int|long|None) – If None, it makes no effect to lookup. -Otherwise the given padding_idx indicates padding the output -with zeros whenever lookup encounters it in input. If -\(padding_idx < 0\), the padding_idx to use in lookup is -\(size[0] + dim\).
      • -
    • param_attr (ParamAttr) – Parameters for this layer
      • -
    • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
      • +
    • in_true (tuple|list|None) – The True branch to be merged.
      • +
    • in_false (tuple|list|None) – The False branch to be merged.
      • +
    • x (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
      • +
    • mask (list) – A bool column vector which masks the input.
      • +
    • level (int) – The specific lod level to rank.
    Returns:

    The tensor variable storing the embeddings of the supplied inputs.

    +
    Returns:

    The merged output tensor.
    Return type:

    Variable

    @@ -346,344 +298,209 @@ with zeros whenever lookup encounters it in Examples

    -
    dict_size = len(dataset.ids)
-data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
-fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
+
    x = layers.data(
+            name='x', shape=[1], dtype='float32', stop_gradient=False)
+y = layers.data(
+      name='y', shape=[1], dtype='bool', stop_gradient=False)
+
+level = 0
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
+out = layers.merge_lod_tensor(
+      in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
    -
    -

    dynamic_lstm

    -
    +
    +

    BlockGuard

    +
    -paddle.v2.fluid.layers.dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32', name=None)
    -

    Dynamic LSTM Layer

    -

    The defalut implementation is diagonal/peephole connection -(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:

    -
    -\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\end{aligned}\end{align} \]
    -

    where the \(W\) terms denote weight matrices (e.g. \(W_{xi}\) is -the matrix of weights from the input gate to the input), \(W_{ic}, W_{fc}, W_{oc}\) are diagonal weight matrices for peephole connections. In -our implementation, we use vectors to reprenset these diagonal weight -matrices. The \(b\) terms denote bias vectors (\(b_i\) is the input -gate bias vector), \(\sigma\) is the non-linear activations, such as -logistic sigmoid function, and \(i, f, o\) and \(c\) are the input -gate, forget gate, output gate, and cell activation vectors, respectively, -all of which have the same size as the cell output activation vector \(h\).

    -

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) -and \(act_h\) are the cell input and cell output activation functions -and tanh is usually used for them. \(\tilde{c_t}\) is also called -candidate hidden state, which is computed based on the current input and -the previous hidden state.

    -

    Set use_peepholes to False to disable peephole connection. The formula -is omitted here, please refer to the paper -http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    -

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) -operations on the input \(x_{t}\) are NOT included in this operator. -Users can choose to use fully-connect layer before LSTM layer.

    +class paddle.v2.fluid.layers.BlockGuard(main_program) +

    BlockGuard class.

    +

    BlockGuard class is used to create a sub-block in a program by +using the Python with keyword.

    +
    + +
    +
    +

    BlockGuardWithCompletion

    +
    +
    +class paddle.v2.fluid.layers.BlockGuardWithCompletion(rnn)
    +

    BlockGuardWithCompletion class.

    +

    BlockGuardWithCompletion class is used to create an op with a block in a program.

    +
    + +
    + -
    -

    dynamic_lstmp

    +
    +

    WhileGuard

    +
    +
    +class paddle.v2.fluid.layers.WhileGuard(while_op)
    +
    + +
    +
    +

    While

    +
    +
    +class paddle.v2.fluid.layers.While(cond, name=None)
    +
    + +
    +
    +

    lod_rank_table

    -paddle.v2.fluid.layers.dynamic_lstmp(input, size, proj_size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', proj_activation='tanh', dtype='float32', name=None)
    -

    Dynamic LSTMP Layer

    -

    LSTMP (LSTM with recurrent projection) layer has a separate projection -layer after the LSTM layer, projecting the original hidden state to a -lower-dimensional one, which is proposed to reduce the number of total -parameters and furthermore computational complexity for the LSTM, -espeacially for the case that the size of output units is relative -large (https://research.google.com/pubs/archive/43905.pdf).

    -

    The formula is as follows:

    -
    -\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\\r_t & = \overline{act_h}(W_{rh}h_t)\end{aligned}\end{align} \]
    -

    In the above formula:

    -
      -
    • \(W\): Denotes weight matrices (e.g. \(W_{xi}\) is the matrix of weights from the input gate to the input).
      • -
    • \(W_{ic}\), \(W_{fc}\), \(W_{oc}\): Diagonal weight matrices for peephole connections. In our implementation, we use vectors to reprenset these diagonal weight matrices.
      • -
    • \(b\): Denotes bias vectors (e.g. \(b_i\) is the input gate bias vector).
      • -
    • \(\sigma\): The activation, such as logistic sigmoid function.
      • -
    • \(i, f, o\) and \(c\): The input gate, forget gate, output gate, and cell activation vectors, respectively, all of which have the same size as the cell output activation vector \(h\).
      • -
    • \(h\): The hidden state.
      • -
    • \(r\): The recurrent projection of the hidden state.
      • -
    • \(\tilde{c_t}\): The candidate hidden state, whose computation is based on the current input and previous hidden state.
      • -
    • \(\odot\): The element-wise product of the vectors.
      • -
    • \(act_g\) and \(act_h\): The cell input and cell output activation functions and tanh is usually used for them.
      • -
    • \(\overline{act_h}\): The activation function for the projection output, usually using identity or same as \(act_h\).
      • -
    -

    Set use_peepholes to False to disable peephole connection. The formula -is omitted here, please refer to the paper -http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    -

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) -operations on the input \(x_{t}\) are NOT included in this operator. -Users can choose to use fully-connected layer before LSTMP layer.

    +paddle.v2.fluid.layers.lod_rank_table(x, level=0) +

    LoD Rank Table Operator. Given an input variable x and a level number +of LoD, this layer creates a LodRankTable object. A LoDRankTable object +contains a list of bi-element tuples. Each tuple consists of an index and +a length, both of which are int type. Refering to specified level of LoD, +the index is the sequence index number and the length representes the +sequence length. Please note that the list is ranked in descending order by +the length. The following is an example:

    +
    +
    x is a LoDTensor:
+    x.lod = [[0,                2, 3],
+             [0,             5, 6, 7]]
+    x.data = [a, b, c, d, e, f, g]
+
+1. set level to 0:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=0)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 2), (1, 1)]
+
+2. set level to 1:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=1)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]
+
    +
    +
    - -
    Parameters:
      -
    • input (Variable) – The input of dynamic_lstmp layer, which supports -variable-time length input sequence. The underlying -tensor in this Variable is a matrix with shape -(T X 4D), where T is the total time steps in this -mini-batch, D is the hidden size.
      • -
    • size (int) – 4 * hidden size.
      • -
    • proj_size (int) – The size of projection output.
      • -
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable -hidden-hidden weight and projection weight.

      -
        -
      • Hidden-hidden weight = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}.
        • -
      • The shape of hidden-hidden weight is (P x 4D), -where P is the projection size and D the hidden -size.
        • -
      • Projection weight = {\(W_{rh}\)}.
        • -
      • The shape of projection weight is (D x P).
        • -
      -
      • -
    • bias_attr (ParamAttr|None) –

      The bias attribute for the learnable bias -weights, which contains two parts, input-hidden -bias weights and peephole connections weights if -setting use_peepholes to True.

      -
        -
      1. use_peepholes = False
        2. -
      -
      -
        -
      • Biases = {\(b_c, b_i, b_f, b_o\)}.
        • -
      • The shape is (1 x 4D).
        • -
      -
      -
        -
      1. use_peepholes = True
        2. -
      -
      -
        -
      • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
        • -
      • The shape is (1 x 7D).
        • -
      -
      -
      • -
    • use_peepholes (bool) – Whether to enable diagonal/peephole connections, -default True.
      • -
    • is_reverse (bool) – Whether to compute reversed LSTM, default False.
      • -
    • gate_activation (str) – The activation for input gate, forget gate and -output gate. Choices = [“sigmoid”, “tanh”, “relu”, -“identity”], default “sigmoid”.
      • -
    • cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, -“tanh”, “relu”, “identity”], default “tanh”.
      • -
    • candidate_activation (str) – The activation for candidate hidden state. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], -default “tanh”.
      • -
    • proj_activation (str) – The activation for projection output. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], -default “tanh”.
      • -
    • dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x (Variable) – Input variable, a LoDTensor based which to create the lod +rank table.
      • +
    • level (int) – Specify the LoD level, on which to create the lod rank +table.
    Returns:

    The projection of hidden state, and cell state of LSTMP. The shape of projection is (T x P), for the cell state which is (T x D), and both LoD is the same with the input.

    +
    Returns:

    The created LoDRankTable object.
    Return type:

    tuple

    +
    Return type:

    Variable

    Examples

    -
    hidden_dim, proj_dim = 512, 256
-fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
-                         act=None, bias_attr=None)
-proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
-                                         size=hidden_dim * 4,
-                                         proj_size=proj_dim,
-                                         use_peepholes=False,
-                                         is_reverse=True,
-                                         cell_activation="tanh",
-                                         proj_activation="tanh")
+
    x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+out = layers.lod_rank_table(x=x, level=0)
    -
    -

    dynamic_gru

    +
    +

    max_sequence_len

    -paddle.v2.fluid.layers.dynamic_gru(input, size, param_attr=None, bias_attr=None, is_reverse=False, gate_activation='sigmoid', candidate_activation='tanh', h_0=None)
    -

    Dynamic GRU Layer

    -

    Refer to Empirical Evaluation of Gated Recurrent Neural Networks on -Sequence Modeling

    -

    The formula is as follows:

    -
    -\[ \begin{align}\begin{aligned}u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)\\r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)\\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)\\h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \tilde{h_t}\end{aligned}\end{align} \]
    -

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) -is the update gate and reset gate activation function and \(sigmoid\) -is usually used for it. \(act_c\) is the activation function for -candidate hidden state and \(tanh\) is usually used for it.

    -

    Note that these \(W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}\) operations on -the input \(x_{t}\) are NOT included in this operator. Users can choose -to use fully-connect layer before GRU layer.

    +paddle.v2.fluid.layers.max_sequence_len(rank_table) +

    Max Sequence Len Operator. Given a LoDRankTable object, this layer +returns the max length of a batch of sequences. In fact, a LoDRankTable +object contains a list of tuples(<sequence index, sequence length>) and +the list is already sorted by sequence length in descending order, so the +operator just returns the sequence length of the first tuple element.

    - + - + - +
    Parameters:
      -
    • input (Variable) – The input of dynamic_gru layer, which supports -variable-time length input sequence. The underlying tensor in this -Variable is a matrix with shape \((T \times 3D)\), where -\(T\) is the total time steps in this mini-batch, \(D\) -is the hidden size.
      • -
    • size (int) – The dimension of the gru cell.
      • -
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable -hidden-hidden weight matrix. Note:

      -
        -
      • The shape of the weight matrix is \((T \times 3D)\), where -\(D\) is the hidden size.
        • -
      • All elements in the weight matrix can be divided into two parts. -The first part are weights of the update gate and reset gate with -shape \((D \times 2D)\), and the second part are weights for -candidate hidden state with shape \((D \times D)\).
        • -
      -
      • -
    • bias_attr (ParamAttr) – The parameter attribute for learnable the -hidden-hidden bias.
      • -
    • is_reverse (bool) – Whether to compute reversed GRU, default -False.
      • -
    • gate_activation (str) – The activation for update gate and reset gate. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “sigmoid”.
      • -
    • activation (str) – The activation for candidate hidden state. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “tanh”.
      • -
    -
    Parameters:rank_table (Variable) – Input variable which is a LoDRankTable object.
    Returns:

    The hidden state of GRU. The shape is (T times D), and lod is the same with the input.

    -
    Returns:The max length of sequence.
    Return type:

    Variable

    -
    Return type:Variable

    Examples

    -
    hidden_dim = 512
-x = fluid.layers.fc(input=data, size=hidden_dim * 3)
-hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
+
    x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+rank_table = layers.lod_rank_table(x=x, level=0)
+max_seq_len = layers.max_sequence_len(rank_table)
    -
    -

    data

    +
    +

    topk

    -paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, stop_gradient=True)
    -

    Data Layer

    -

    This function takes in the input and based on whether data has -to be returned back as a minibatch, it creates the global variable by using -the helper functions. The global variables can be accessed by all the -following operators in the graph.

    -

    All the input variables of this function are passed in as local variables -to the LayerHelper constructor.

    +paddle.v2.fluid.layers.topk(input, k) +

    topk

    +

    This function performs the operation that selects the k entries in the input +vector and outputs their values and indices as vectors. Thus topk_out[j] is +the j-th largest entry in input, and its index is topk_indices[j]

    -
    Parameters:
      -
    • name (str) – The name/alias of the function
      • -
    • shape (list) – Tuple declaring the shape.
      • -
    • append_batch_size (bool) – Whether or not to append the data as a batch.
      • -
    • dtype (int|float) – The type of data : float32, float_16, int etc
      • -
    • type (VarType) – The output type. By default it is LOD_TENSOR.
      • -
    • lod_level (int) – The LoD Level. 0 means the input data is not a sequence.
      • -
    • main_program (Program) – Name of the main program that calls this
      • -
    • startup_program (Program) – Name of the startup program
      • -
    • stop_gradient (bool) – A boolean that mentions whether gradient should flow.
      • +
    • input (Variable|list) – The input tensor that has all the data.
      • +
    • k (int) – The number of top elements that the function will pick.
    Returns:

    The global variable that gives access to the data.

    +
    Returns:

    +
    The variable of type array that contains the k largest entries
    +

    from input.

    +
    +
    Variable: The variable of type array that contains the indices of k
    +

    largest entries from input.

    +
    +
    +
    Return type:

    Variable

    @@ -692,399 +509,388 @@ to the LayerHelper constructor.

    Examples

    -
    data = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
    x = fluid.layers.data(name='x', shape=[10])
+k = 5
+array = fluid.layers.topk(x, k)
    -
    -

    mean

    +
    +

    lod_tensor_to_array

    -paddle.v2.fluid.layers.mean(**kwargs)
    -

    Mean Operator.

    -

    Out is a scalar which is the mean of all elements in X.

    +paddle.v2.fluid.layers.lod_tensor_to_array(x, table) +

    Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.

    - - - + - -
    Parameters:x – The input of mean op -Duplicable: False Optional: False
    Returns:The output of mean op
    Parameters:
      +
    • x (Variable|list) – The LOD tensor to be converted to a LOD tensor array.
      • +
    • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
      • +
    +
    -
    +
    Returns:

    +
    The variable of type array that has been converted from a
    +

    tensor.

    +
    +
    +

    +
    Return type:

    Variable

    +
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+
    +
    +
    -
    -

    mul

    +
    +

    array_to_lod_tensor

    -paddle.v2.fluid.layers.mul(**kwargs)
    -

    Mul Operator.

    -

    This operator is used to perform matrix multiplication for input $X$ and $Y$.

    -

    The equation is:

    -

    $$Out = X * Y$$

    -

    Both the input $X$ and $Y$ can carry the LoD (Level of Details) information, -or not. But the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.array_to_lod_tensor(x, table) +

    Convert a LoD_Tensor_Aarry to an LoDTensor.

    - + +
    Parameters:
      -
    • x – (Tensor), The first input tensor of mul op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of mul op. -Duplicable: False Optional: False
      • -
    • x_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two -dimensions as its inputs. If the input $X$ is a tensor with more -than two dimensions, $X$ will be flattened into a two-dimensional -matrix first. The flattening rule is: the first num_col_dims -will be flattened to form the first dimension of the final matrix -(the height of the matrix), and the rest rank(X) - num_col_dims -dimensions are flattened to form the second dimension of the final -matrix (the width of the matrix). As a result, height of the -flattened matrix is equal to the product of $X$’s first -x_num_col_dims dimensions’ sizes, and width of the flattened -matrix is equal to the product of $X$’s last rank(x) - num_col_dims -dimensions’ size. For example, suppose $X$ is a 6-dimensional -tensor with the shape [2, 3, 4, 5, 6], and x_num_col_dims = 3. -Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = -[24, 30].
      • -
    • y_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two, -dimensions as its inputs. If the input $Y$ is a tensor with more -than two dimensions, $Y$ will be flattened into a two-dimensional -matrix first. The attribute y_num_col_dims determines how $Y$ is -flattened. See comments of x_num_col_dims for more details.
      • +
    • x (Variable|list) – The lod tensor array to be converted to a tensor.
      • +
    • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
    Returns:

    (Tensor), The output tensor of mul op.

    +
    Returns:

    +
    The variable of type tensor that has been converted
    +

    from an array.

    +
    +
    +

    +
    Return type:

    Variable
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
+
    +
    -
    -

    elementwise_add

    +
    +

    increment

    -paddle.v2.fluid.layers.elementwise_add(**kwargs)
    -

    Limited Elementwise Add Operator.

    -

    The equation is:

    -

    $$Out = X + Y$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-
    -
    -
    -
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True) +

    This function performs an operation that increments each value in the +input \(x\) by an amount: \(value\) as mentioned in the input +parameter. This operation is performed in-place by default.

    - + +
    Parameters:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    • x (Variable|list) – The tensor that has the input values.
      • +
    • value (float) – The amount by which the values should be incremented.
      • +
    • in_place (bool) – If the increment should be performed in-place.
    Returns:

    The output of elementwise op.

    +
    Returns:

    +
    The tensor variable storing the transformation of
    +

    element-wise increment of each value in the input.

    +
    +
    +

    +
    Return type:

    Variable
    +

    Examples

    +
    data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
+data = fluid.layers.increment(x=data, value=3.0, in_place=True)
+
    +
    -
    -

    elementwise_sub

    +
    +

    array_write

    -paddle.v2.fluid.layers.elementwise_sub(**kwargs)
    -

    Limited Elementwise Sub Operator.

    -

    The equation is:

    -

    $$Out = X - Y$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-
    -
    -
    -
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.array_write(x, i, array=None) +

    This function writes the given input variable to the specified position +indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the +output LOD_TENSOR_ARRAY is not given(None), a new one will be created and +returned.

    - + +
    Parameters:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    • x (Variable|list) – The input tensor from which the data will be read.
      • +
    • i (Variable|list) – The index of the output LOD_TENSOR_ARRAY, pointing to +the position to which the input tensor will be +written.
      • +
    • array (Variable|list) – The output LOD_TENSOR_ARRAY to which the input +tensor will be written. If this parameter is +NONE, a new LOD_TENSOR_ARRAY will be created and +returned.
    Returns:

    The output of elementwise op.

    +
    Returns:

    The output LOD_TENSOR_ARRAY where the input tensor is written.

    +
    Return type:

    Variable
    +

    Examples

    -
    -

    elementwise_mul

    +
    +

    create_array

    -paddle.v2.fluid.layers.elementwise_mul(**kwargs)
    -

    Limited Elementwise Mul Operator.

    -

    The equation is:

    -

    $$Out = X odotY$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+paddle.v2.fluid.layers.create_array(dtype)
+
    This function creates an array of type \(LOD_TENSOR_ARRAY\) using the
+LayerHelper.
    
+
+++
+
+
+
+
+
+
+
+
    


    Parameters:dtype (int|float) – The data type of the elements in the array.
    Returns:The tensor variable storing the elements of data type.
    Return type:Variable
    
+
    Examples
    
+
    data = fluid.layers.create_array(dtype='float32')
 
    
 
    
-
    
-
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +
    + +
    +
    +

    less_than

    +
    +
    +paddle.v2.fluid.layers.less_than(x, y, cond=None, **ignored)
    +

    Less than

    +

    This layer returns the truth value of \(x < y\) elementwise.

    - + +
    Parameters:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    • x (Variable) – First operand of less_than
      • +
    • y (Variable) – Second operand of less_than
      • +
    • cond (Variable|None) – Optional output variable to store the result of less_than
    Returns:

    The output of elementwise op.

    +
    Returns:

    The tensor variable storing the output of less_than.

    +
    Return type:

    Variable
    +

    Examples

    +
    less = fluid.layers.less_than(x=label, y=limit)
+
    +
    -
    -

    elementwise_div

    +
    +

    array_read

    -paddle.v2.fluid.layers.elementwise_div(**kwargs)
    -

    Limited Elementwise Div Operator.

    -

    The equation is:

    -

    $$Out = X / Y$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-
    -
    -
    -
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.array_read(array, i) +

    This function performs the operation to read the data in as an +LOD_TENSOR_ARRAY. +:param array: The input tensor that will be written to an array. +:type array: Variable|list +:param i: The subscript index in tensor array, that points the

    +
    +
    place where data will be written to.
    - + - +
    Parameters:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • -
    -
    Returns:The tensor type variable that has the data written to it.
    Returns:

    The output of elementwise op.

    -
    Return type:Variable
    +

    Examples

    -
    -

    dropout

    +
    +

    shrink_memory

    -paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=None, **kwargs)
    -

    Computes dropout.

    -

    Drop or keep each element of x independently. Dropout is a regularization -technique for reducing overfitting by preventing neuron co-adaption during -training. The dropout operator randomly set (according to the given dropout -probability) the outputs of some units to zero, while others are remain -unchanged.

    +paddle.v2.fluid.layers.shrink_memory(x, i, table) +

    This function creates an operator to shrink_rnn_memory using the RankTable +as mentioned in the input parameter.

    +
    + +
    +
    +

    array_length

    +
    +
    +paddle.v2.fluid.layers.array_length(array)
    +

    This function performs the operation to find the length of the input +LOD_TENSOR_ARRAY.

    - + - + - +
    Parameters:
      -
    • x (variable) – The input tensor.
      • -
    • dropout_prob (float) – Probability of setting units to zero.
      • -
    • is_test (bool) – A flag indicating whether it is in test phrase or not.
      • -
    • seed (int) – A Python integer used to create random seeds. If this -parameter is set to None, a random seed is used. -NOTE: If an integer seed is given, always the same output -units will be dropped. DO NOT use a fixed seed in training.
      • -
    -
    Parameters:array (LOD_TENSOR_ARRAY) – The input array that will be used +to compute the length.
    Returns:

    A tensor variable.

    -
    Returns:The length of the input LoDTensorArray.
    Return type:

    Variable

    -
    Return type:Variable

    Examples

    -
    x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
-droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
-
    -
    -
    -

    reshape

    -
    +
    +

    IfElse

    +
    -paddle.v2.fluid.layers.reshape(**kwargs)
    -

    Reshape Operator.

    -

    Reshape Input(X) into the shape specified by Attr(shape).

    -

    An example: -Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]

    -

    and target shape = [1, 4], the reshape operator will transform -the tensor X into a 2-D tensor: [[1, 2, 3, 4]]

    -

    One dimension in the target shape can be set -1, representing that its -size is unknown. In this case, the real dimension will be infered from -the original shape of Input(X) and other dimensions in the target shape.

    - +class paddle.v2.fluid.layers.IfElse(cond, name=None) +
    + + +
    +

    DynamicRNN

    +
    +
    +class paddle.v2.fluid.layers.DynamicRNN(name=None)
    +
    + +
    +
    +

    ConditionalBlock

    +
    +
    +class paddle.v2.fluid.layers.ConditionalBlock(inputs, name=None)
    +
    + +
    +
    +

    StaticRNN

    +
    +
    +class paddle.v2.fluid.layers.StaticRNN(name=None)
    +

    StaticRNN class.

    +

    StaticRNN class is used to create a StaticRNN. The RNN will have its +own parameters like inputs, outputs, memories, status and length.

    +
    +
    +memory(init=None, shape=None, batch_ref=None, init_value=0.0, init_batch_dim_idx=0, ref_batch_dim_idx=1)
    +
    - - -
    Parameters:
      -
    • x – The input tensor of reshape operator. -Duplicable: False Optional: False
      • -
    • shape (INTS) – (vector<int>) Target shape of reshape operator.
      • +
    Parameters:
      +
    • init – boot memory, if not set, a shape, batch_ref must be provided
      • +
    • shape – shape of the boot memory
      • +
    • batch_ref – batch size reference variable
      • +
    • init_value – the init value of boot memory
      • +
    • init_batch_dim_idx – the index of batch size in init’s dimension
      • +
    • ref_batch_dim_idx – the index of batch size in batch_ref’s dimension
    Returns:

    The output tensor of reshape operator.

    -
    -
    -
    -

    sigmoid

    -
    -
    -paddle.v2.fluid.layers.sigmoid(**kwargs)
    -

    Sigmoid Activation Operator

    -

    $$out = frac{1}{1 + e^{-x}}$$

    - --- - - - - - -
    Parameters:x – Input of Sigmoid operator -Duplicable: False Optional: False
    Returns:Output of Sigmoid operator
    -
    -

    scale

    +
    +

    reorder_lod_tensor_by_rank

    -paddle.v2.fluid.layers.scale(**kwargs)
    -

    Scale operator

    -

    $$Out = scale*X$$

    +paddle.v2.fluid.layers.reorder_lod_tensor_by_rank(x, rank_table) +

    ReorderLoDTensorByRankTable operator.

    +

    Input(X) is a batch of sequences. Input(RankTable) stores new orders of the +input sequence batch. The reorder_lod_tensor_by_rank operator reorders the +Input(X) according to the information provided by Input(RankTable).

    +

    For example:

    +

    If the indices stored in the Input(RankTable) are [3, 0, 2, 1], the +Input(X) will be reordered that the fourth sequence in Input(X) will become the +first one, and then followed by the original first, third, and the second one.

    +

    This is: +X = [Seq0, Seq1, Seq2, Seq3]. The indices in RankTable are [3, 0, 2, 1]. +Out = [Seq3, Seq0, Seq2, Seq1] with a new LoD information.

    +

    If the LoD information of Input(X) is empty, this means Input(X) is not sequence +data. This is also identical to a batch of sequences where each sequence has a +fixed length 1. In this case, the reorder_lod_tensor_by_rank operator reorders +each slice of Input(X) along the first axis according to Input(RankTable).

    +

    This is: +X = [Slice0, Slice1, Slice2, Slice3] and its LoD information is empty. The +indices in RankTable are [3, 0, 2, 1]. +Out = [Slice3, Slice0, Slice2, Slice1] with no LoD information is appended.

    +

    NOTE: This operator sorts Input(X) according to a given LoDRankTable which does +not need to be calculated according to Input(X). It can be calculated according +to another different sequence, and then this operator sorts Input(X) according +to the given LoDRankTable.

    - @@ -1092,26 +898,47 @@ Duplicable: False Optional: False -
    -

    transpose

    +
    +

    ParallelDo

    +
    +
    +class paddle.v2.fluid.layers.ParallelDo(places, name=None)
    +

    ParallelDo class.

    +

    ParallelDo class is used to create a ParallelDo.

    +
    + +
    +
    +

    Print

    -paddle.v2.fluid.layers.transpose(x, perm, name=None)
    -

    transpose Layer

    -

    Permute the dimensions of input according to perm.

    -

    The i-th dimension of the returned tensor will correspond to the -perm[i]-th dimension of input.

    +paddle.v2.fluid.layers.Print(input, first_n=-1, message=None, summarize=-1, print_tensor_name=True, print_tensor_type=True, print_tensor_shape=True, print_tensor_lod=True, print_phase='both') +

    Print operator

    +

    This creates a print op that will print when a tensor is accessed.

    +

    Wraps the tensor passed in so that whenever that a tensor is accessed, +the message message is printed, along with the current value of the +tensor t.

    Parameters:
      -
    • x – (Tensor) Input tensor of scale operator. +
    • x – (LoDTensor), the input lod tensor to be reordered according to Input(RankTable). +Duplicable: False Optional: False
      • +
    • rank_table – (LoDRankTable), the rank table according to which Input(X) is reordered. Duplicable: False Optional: False
      • -
    • scale (FLOAT) – (float, default 1.0)The scaling factor of the scale operator.
    Returns:

    (Tensor) Output tensor of scale operator.

    +
    Returns:

    (LoDTensor), the reordered lod tensor.
    -
    Parameters:
      -
    • input (Variable) – (Tensor), A Tensor.
      • -
    • perm (list) – A permutation of the dimensions of input.
      • +
    • input (Variable) – A Tensor to print.
      • +
    • summarize (int) – Print this number of elements in the tensor, will print +all if left is negative.
      • +
    • message (str) – A string message to print as a prefix.
      • +
    • first_n (int) – Only log first_n number of times.
      • +
    • print_tensor_name (bool) – Print the tensor name.
      • +
    • print_tensor_type (bool) – Print the tensor type.
      • +
    • print_tensor_shape (bool) – Print the tensor shape.
      • +
    • print_tensor_lod (bool) – Print the tensor lod.
      • +
    • print_phase (bool) – Which phase to displace, including ‘forward’, +‘backward’ and ‘both’. If set to ‘backward’ or ‘both’, will +print the gradients of input tensor.
    Returns:

    A transposed Tensor.

    +
    Returns:

    Output tensor, same data with input tensor.
    Return type:

    Variable

    @@ -1120,210 +947,260 @@ perm[i]-th dimension of input.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
-x_transposed = layers.transpose(x, perm=[1, 0, 2])
+
    
 
    
 
    
+
    value = some_layer(...)
+Print(value, summarize=10,
    
+
    
+
    message=”The content of some_layer: ”)
    -
    -

    sigmoid_cross_entropy_with_logits

    -
    -
    -

    cast

    -
    -
    -paddle.v2.fluid.layers.cast(x, dtype)
    -

    This function takes in the input with input_dtype -and casts it to the output_dtype as the output.

    -
    -
    -
    -

    concat

    +
    +

    device

    +
    +

    get_places

    -paddle.v2.fluid.layers.concat(input, axis=0)
    -

    Concat

    -

    This function concatenates the input along the axis mentioned -and returns that as the output.

    +paddle.v2.fluid.layers.get_places(device_count=None, device_type=None) +

    Returns a list of places based on flags. The list will be used for parallel +execution.

    - - -
    Parameters:
      -
    • input (list) – List of tensors to be concatenated
      • -
    • axis (int) – Integer axis along which the tensors will be concatenated
      • +
    • device_count (INT) – device count
      • +
    • device_type (STRING) – device type
    Returns:

    Output variable of the concatenation

    -
    Return type:

    Variable

    +
    Returns:

    vector of Place
    -

    Examples

    -
    -

    sums

    +
    +
    +

    io

    +
    +

    data

    -paddle.v2.fluid.layers.sums(input, out=None)
    -

    This function performs the sum operation on the input and returns the -result as the output.

    +paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, stop_gradient=True) +

    Data Layer

    +

    This function takes in the input and based on whether data has +to be returned back as a minibatch, it creates the global variable by using +the helper functions. The global variables can be accessed by all the +following operators in the graph.

    +

    All the input variables of this function are passed in as local variables +to the LayerHelper constructor.

    - + - - +
    Parameters:input (Variable|list) – The input tensor that has the elements -that need to be summed up.
    Parameters:
      +
    • name (str) – The name/alias of the function
      • +
    • shape (list) – Tuple declaring the shape.
      • +
    • append_batch_size (bool) – Whether or not to append the data as a batch.
      • +
    • dtype (int|float) – The type of data : float32, float_16, int etc
      • +
    • type (VarType) – The output type. By default it is LOD_TENSOR.
      • +
    • lod_level (int) – The LoD Level. 0 means the input data is not a sequence.
      • +
    • main_program (Program) – Name of the main program that calls this
      • +
    • startup_program (Program) – Name of the startup program
      • +
    • stop_gradient (bool) – A boolean that mentions whether gradient should flow.
      • +
    +
    Returns:
    -
    The tensor type variable that has the sum of input
    -
    written to it.
    -
    +
    Returns:

    The global variable that gives access to the data.
    Return type:Variable
    Return type:

    Variable

    +

    Examples

    +
    data = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
    +
    -
    -

    linear_chain_crf

    -
    +
    +

    BlockGuardServ

    +
    -paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)
    -
    +class paddle.v2.fluid.layers.BlockGuardServ(server) +

    BlockGuardServ class.

    +

    BlockGuardServ class is used to create an op with a block in a program.

    +
    -
    -

    assign

    +
    +

    ListenAndServ

    +
    +
    +class paddle.v2.fluid.layers.ListenAndServ(endpoint, fan_in=1, optimizer_mode=True)
    +

    ListenAndServ class.

    +

    ListenAndServ class is used to wrap listen_and_serv op to create a server +which can receive variables from clients and run a block.

    +
    + +
    +
    +

    Send

    -paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, padding_idx=None, param_attr=None, dtype='float32')
    -

    Embedding Layer

    -

    This layer is used to lookup embeddings of IDs, provided by input, in -a lookup table. The result of this lookup is the embedding of each ID in the -input.

    -

    All the input variables are passed in as local variables to the LayerHelper -constructor.

    +paddle.v2.fluid.layers.Send(endpoints, send_vars, get_vars) +

    Send layer

    - - - - -
    Parameters:
      -
    • input (Variable) – The tensor variable containing the IDs.
      • -
    • size (tuple|list) – The shape of the look up table parameter. It should -have two elements which indicate the size of the dictionary of -embeddings and the size of each embedding vector respectively.
      • -
    • is_sparse (bool) – The flag indicating whether to use sparse update.
      • -
    • padding_idx (int|long|None) – If None, it makes no effect to lookup. -Otherwise the given padding_idx indicates padding the output -with zeros whenever lookup encounters it in input. If -\(padding_idx < 0\), the padding_idx to use in lookup is -\(size[0] + dim\).
      • -
    • param_attr (ParamAttr) – Parameters for this layer
      • -
    • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
      • +
    Parameters:
      +
    • endpoints – comma seperated IP:PORT pairs in the order +of send_vars to send
      • +
    • send_vars – vars to send
      • +
    • get_vars – vars to get from server after send completes.
    Returns:

    The tensor variable storing the embeddings of the supplied inputs.

    -
    Return type:

    Variable

    -
    -

    Examples

    -
    dict_size = len(dataset.ids)
-data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
-fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
-
    -
    +

    Send variables to the server side, and get vars from server +side when server have finished running server side program.

    -
    -

    split_lod_tensor

    +
    +
    +

    nn

    +
    +

    fc

    -paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0)
    -

    split_lod_tensor

    -

    This function takes in an input that contains the complete lod information, -and takes in a mask which is used to mask certain parts of the input. -The output is the true branch and the false branch with the mask applied to -the input at a certain level in the tensor.

    +paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None) +

    Fully Connected Layer

    +

    The fully connected layer can take multiple tensors as its inputs. It +creates a variable (one for each input tensor) called weights for each +input tensor, which represents a fully connected weight matrix from +each input unit to each output unit. The fully connected layer +multiplies each input tensor with its coresponding weight to produce +an output Tensor. If multiple input tensors are given, the results of +multiple multiplications will be sumed up. If bias_attr is not None, +a biases variable will be created and added to the output. Finally, +if activation is not None, it will be applied to the output as well.

    +

    This process can be formulated as follows:

    +
    +\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]
    +

    In the above equation:

    +
      +
    • \(N\): Number of the input.
      • +
    • \(X_i\): The input tensor.
      • +
    • \(W\): The weights created by this layer.
      • +
    • \(b\): The bias parameter created by this layer (if needed).
      • +
    • \(Act\): The activation funtion.
      • +
    • \(Out\): The output tensor.
      • +
    - - + +
    Parameters:
      -
    • input (tuple|list|None) – The input tensor that contains complete -lod information needed to construct the output.
      • -
    • mask (list) – A bool column vector which masks the input.
      • -
    • level (int) – The specific lod level to rank.
      • +
    • input (Variable|list) – The input tensor(s) to the fully connected layer.
      • +
    • size (int) – The number of output units in the fully connected layer.
      • +
    • num_flatten_dims (int) – The fc layer can accept an input tensor with more +than two dimensions. If this happens, the +multidimensional tensor will first be flattened +into a 2-dimensional matrix. The parameter +num_flatten_dims determines how the input tensor +is flattened: the first num_flatten_dims +(inclusive, index starts from 1) dimensions will +be flatten to form the first dimension of the +final matrix (height of the matrix), and the rest +rank(X) - num_flatten_dims dimensions are +flattened to form the second dimension of the +final matrix (width of the matrix). For example, +suppose X is a 6-dimensional tensor with a shape +[2, 3, 4, 5, 6], and num_flatten_dims = 3. Then, +the flattened matrix will have a shape +[2 x 3 x 4, 5 x 6] = [24, 30]. By default, +num_flatten_dims is set to 1.
      • +
    • param_attr (ParamAttr|list) – The parameter attribute for learnable +parameters/weights of the fully connected +layer.
      • +
    • param_initializer (ParamAttr|list) – The initializer used for the +weight/parameter. If set None, +XavierInitializer() will be used.
      • +
    • bias_attr (ParamAttr|list) – The parameter attribute for the bias parameter +for this layer. If set None, no bias will be +added to the output units.
      • +
    • bias_initializer (ParamAttr|list) – The initializer used for the bias. +If set None, then ConstantInitializer() +will be used.
      • +
    • act (str) – Activation to be applied to the output of the fully connected +layer.
      • +
    • name (str) – Name/alias of the fully connected layer.
    Returns:

    The true branch of tensor as per the mask applied to input. -Variable: The false branch of tensor as per the mask applied to input.

    +
    Returns:

    The output tensor variable.
    Return type:

    Variable

    +
    Return type:

    Variable

    +
    Raises:

    ValueError – If rank of the input tensor is less than 2.

    Examples

    -
    x = layers.data(name='x', shape=[1])
-x.persistable = True
-
-y = layers.data(name='y', shape=[1])
-y.persistable = True
-
-out_true, out_false = layers.split_lod_tensor(
-      input=x, mask=y, level=level)
+
    data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+fc = fluid.layers.fc(input=data, size=1000, act="tanh")
    -
    -

    merge_lod_tensor

    +
    +

    embedding

    -paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0)
    -

    merge_lod_tensor

    -

    This function takes in an input \(x\), the True branch, the False -branch and a binary \(mask\). Using this information, this function -merges the True and False branches of the tensor into a single Output -at a certain lod level indiacted by \(level\).

    +paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, padding_idx=None, param_attr=None, dtype='float32') +

    Embedding Layer

    +

    This layer is used to lookup embeddings of IDs, provided by input, in +a lookup table. The result of this lookup is the embedding of each ID in the +input.

    +

    All the input variables are passed in as local variables to the LayerHelper +constructor.

    -
    Parameters:
      -
    • in_true (tuple|list|None) – The True branch to be merged.
      • -
    • in_false (tuple|list|None) – The False branch to be merged.
      • -
    • x (tuple|list|None) – The input tensor that contains complete -lod information needed to construct the output.
      • -
    • mask (list) – A bool column vector which masks the input.
      • -
    • level (int) – The specific lod level to rank.
      • +
    • input (Variable) – The tensor variable containing the IDs.
      • +
    • size (tuple|list) – The shape of the look up table parameter. It should +have two elements which indicate the size of the dictionary of +embeddings and the size of each embedding vector respectively.
      • +
    • is_sparse (bool) – The flag indicating whether to use sparse update.
      • +
    • padding_idx (int|long|None) – If None, it makes no effect to lookup. +Otherwise the given padding_idx indicates padding the output +with zeros whenever lookup encounters it in input. If +\(padding_idx < 0\), the padding_idx to use in lookup is +\(size[0] + dim\).
      • +
    • param_attr (ParamAttr) – Parameters for this layer
      • +
    • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
    Returns:

    The merged output tensor.

    +
    Returns:

    The tensor variable storing the embeddings of the supplied inputs.
    Return type:

    Variable

    @@ -1332,150 +1209,298 @@ lod information needed to construct the output.

    Examples

    -
    x = layers.data(
-            name='x', shape=[1], dtype='float32', stop_gradient=False)
-y = layers.data(
-      name='y', shape=[1], dtype='bool', stop_gradient=False)
-
-level = 0
-
-out_true, out_false = layers.split_lod_tensor(
-      input=x, mask=y, level=level)
-out = layers.merge_lod_tensor(
-      in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
+
    dict_size = len(dataset.ids)
+data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
+fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
    -
    -

    cos_sim

    -
    -
    -paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)
    -

    This function performs the cosine similarity between two tensors -X and Y and returns that as the output.

    -
    - -
    -
    -

    cross_entropy

    +
    +

    dynamic_lstm

    -paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)
    -

    Cross Entropy Layer

    -

    This layer computes the cross entropy between input and label. It -supports both standard cross-entropy and soft-label cross-entropy loss -computation.

    -
      -
    1. -
      One-hot cross-entropy:
      -

      soft_label = False, Label[i, 0] indicates the class index for sample i:

      -
      -\[Y[i] = -\log(X[i, Label[i]])\]
      -
      -
      -
      2. -
    2. -
      Soft-label cross-entropy:
      -

      soft_label = True, Label[i, j] indicates the soft label of class j -for sample i:

      -
      -\[Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}\]
      -
      -
      -

      Please make sure that in this case the summation of each row of label -equals one.

      -
      3. -
    3. -
      One-hot cross-entropy with vecterized label:
      -

      As a special case of 2), when each row of ‘label’ has only one -non-zero element which is equal to 1, soft-label cross-entropy degenerates -to a one-hot cross-entropy with one-hot label representation.

      -
      -
      -
      4. -
    +paddle.v2.fluid.layers.dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32', name=None) +

    Dynamic LSTM Layer

    +

    The defalut implementation is diagonal/peephole connection +(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:

    +
    +\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\end{aligned}\end{align} \]
    +

    where the \(W\) terms denote weight matrices (e.g. \(W_{xi}\) is +the matrix of weights from the input gate to the input), \(W_{ic}, W_{fc}, W_{oc}\) are diagonal weight matrices for peephole connections. In +our implementation, we use vectors to reprenset these diagonal weight +matrices. The \(b\) terms denote bias vectors (\(b_i\) is the input +gate bias vector), \(\sigma\) is the non-linear activations, such as +logistic sigmoid function, and \(i, f, o\) and \(c\) are the input +gate, forget gate, output gate, and cell activation vectors, respectively, +all of which have the same size as the cell output activation vector \(h\).

    +

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) +and \(act_h\) are the cell input and cell output activation functions +and tanh is usually used for them. \(\tilde{c_t}\) is also called +candidate hidden state, which is computed based on the current input and +the previous hidden state.

    +

    Set use_peepholes to False to disable peephole connection. The formula +is omitted here, please refer to the paper +http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    +

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) +operations on the input \(x_{t}\) are NOT included in this operator. +Users can choose to use fully-connect layer before LSTM layer.

    - - - - + + + +
    Parameters:
      -
    • input (Variable|list) – a 2-D tensor with shape [N x D], where N is the -batch size and D is the number of classes. This -input is a probability computed by the previous -operator, which is almost always the result of -a softmax operator.
      • -
    • label (Variable|list) – the ground truth which is a 2-D tensor. When -soft_label is set to False, label is a -tensor<int64> with shape [N x 1]. When -soft_label is set to True, label is a -tensor<float/double> with shape [N x D].
      • -
    • soft_label (bool, via **kwargs) – a flag indicating whether to -interpretate the given labels as soft -labels, default False.
      • +
    • input (Variable) – The input of dynamic_lstm layer, which supports +variable-time length input sequence. The underlying +tensor in this Variable is a matrix with shape +(T X 4D), where T is the total time steps in this +mini-batch, D is the hidden size.
      • +
    • size (int) – 4 * hidden size.
      • +
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable +hidden-hidden weights.

      +
        +
      • Weights = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}
        • +
      • The shape is (D x 4D), where D is the hidden +size.
      -
    Returns:

    A 2-D tensor with shape [N x 1], the cross entropy loss.

    -
    Raises:

    ValueError – 1) the 1st dimension of input and label are not equal. -2) when soft_label == True, and the 2nd dimension of

    + +
  • bias_attr (ParamAttr|None) –

    The bias attribute for the learnable bias +weights, which contains two parts, input-hidden +bias weights and peephole connections weights if +setting use_peepholes to True.

    +
      +
    1. use_peepholes = False
      2. +
    -

    input and label are not equal.

    -
    -
      -
    1. when soft_label == False, and the 2nd dimension of -label is not 1.
      2. +
        +
      • Biases = {\(b_c, b_i, b_f, b_o\)}.
        • +
      • The shape is (1 x 4D).
        • +
      +
      +
        +
      1. use_peepholes = True
      +
      +
        +
      • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
        • +
      • The shape is (1 x 7D).
        • +
      +
      + +
    2. use_peepholes (bool) – Whether to enable diagonal/peephole connections, +default True.
      3. +
    3. is_reverse (bool) – Whether to compute reversed LSTM, default False.
      4. +
    4. gate_activation (str) – The activation for input gate, forget gate and +output gate. Choices = [“sigmoid”, “tanh”, “relu”, +“identity”], default “sigmoid”.
      5. +
    5. cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, +“tanh”, “relu”, “identity”], default “tanh”.
      6. +
    6. candidate_activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
      7. +
    7. dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
      8. +
    8. name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      9. + +
    • Returns:

    The hidden state, and cell state of LSTM. The shape of both is (T x D), and lod is the same with the input.

    +
    Return type:

    tuple

    Examples

    -
    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
-cost = fluid.layers.cross_entropy(input=predict, label=label)
+
    hidden_dim = 512
+forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                               act=None, bias_attr=None)
+forward, _ = fluid.layers.dynamic_lstm(
+    input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
    -
    -

    square_error_cost

    +
    +

    dynamic_lstmp

    -paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)
    -

    Square error cost layer

    -

    This layer accepts input predictions and target label and returns the -squared error cost.

    -

    For predictions, \(X\), and target labels, \(Y\), the equation is:

    +paddle.v2.fluid.layers.dynamic_lstmp(input, size, proj_size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', proj_activation='tanh', dtype='float32', name=None) +

    Dynamic LSTMP Layer

    +

    LSTMP (LSTM with recurrent projection) layer has a separate projection +layer after the LSTM layer, projecting the original hidden state to a +lower-dimensional one, which is proposed to reduce the number of total +parameters and furthermore computational complexity for the LSTM, +espeacially for the case that the size of output units is relative +large (https://research.google.com/pubs/archive/43905.pdf).

    +

    The formula is as follows:

    -\[Out = (X - Y)^2\]
    -

    In the above equation:

    +\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\\r_t & = \overline{act_h}(W_{rh}h_t)\end{aligned}\end{align} \]
    +

    In the above formula:

    +
      +
    • \(W\): Denotes weight matrices (e.g. \(W_{xi}\) is the matrix of weights from the input gate to the input).
      • +
    • \(W_{ic}\), \(W_{fc}\), \(W_{oc}\): Diagonal weight matrices for peephole connections. In our implementation, we use vectors to reprenset these diagonal weight matrices.
      • +
    • \(b\): Denotes bias vectors (e.g. \(b_i\) is the input gate bias vector).
      • +
    • \(\sigma\): The activation, such as logistic sigmoid function.
      • +
    • \(i, f, o\) and \(c\): The input gate, forget gate, output gate, and cell activation vectors, respectively, all of which have the same size as the cell output activation vector \(h\).
      • +
    • \(h\): The hidden state.
      • +
    • \(r\): The recurrent projection of the hidden state.
      • +
    • \(\tilde{c_t}\): The candidate hidden state, whose computation is based on the current input and previous hidden state.
      • +
    • \(\odot\): The element-wise product of the vectors.
      • +
    • \(act_g\) and \(act_h\): The cell input and cell output activation functions and tanh is usually used for them.
      • +
    • \(\overline{act_h}\): The activation function for the projection output, usually using identity or same as \(act_h\).
      • +
    +

    Set use_peepholes to False to disable peephole connection. The formula +is omitted here, please refer to the paper +http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    +

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) +operations on the input \(x_{t}\) are NOT included in this operator. +Users can choose to use fully-connected layer before LSTMP layer.

    + +++ + + + + + + + +
    Parameters:
      +
    • input (Variable) – The input of dynamic_lstmp layer, which supports +variable-time length input sequence. The underlying +tensor in this Variable is a matrix with shape +(T X 4D), where T is the total time steps in this +mini-batch, D is the hidden size.
      • +
    • size (int) – 4 * hidden size.
      • +
    • proj_size (int) – The size of projection output.
      • +
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable +hidden-hidden weight and projection weight.

      +
        +
      • Hidden-hidden weight = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}.
        • +
      • The shape of hidden-hidden weight is (P x 4D), +where P is the projection size and D the hidden +size.
        • +
      • Projection weight = {\(W_{rh}\)}.
        • +
      • The shape of projection weight is (D x P).
        • +
      +
      • +
    • bias_attr (ParamAttr|None) –

      The bias attribute for the learnable bias +weights, which contains two parts, input-hidden +bias weights and peephole connections weights if +setting use_peepholes to True.

      +
        +
      1. use_peepholes = False
        2. +
      -
        -
      • \(X\): Input predictions, a tensor.
        • -
      • \(Y\): Input labels, a tensor.
        • -
      • \(Out\): Output value, same shape with \(X\).
        • +
          +
        • Biases = {\(b_c, b_i, b_f, b_o\)}.
          • +
        • The shape is (1 x 4D).
          • +
        +
      +
        +
      1. use_peepholes = True
        2. +
      +
      +
        +
      • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
        • +
      • The shape is (1 x 7D).
      +
      • +
    • use_peepholes (bool) – Whether to enable diagonal/peephole connections, +default True.
      • +
    • is_reverse (bool) – Whether to compute reversed LSTM, default False.
      • +
    • gate_activation (str) – The activation for input gate, forget gate and +output gate. Choices = [“sigmoid”, “tanh”, “relu”, +“identity”], default “sigmoid”.
      • +
    • cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, +“tanh”, “relu”, “identity”], default “tanh”.
      • +
    • candidate_activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
      • +
    • proj_activation (str) – The activation for projection output. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
      • +
    • dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    Returns:

    The projection of hidden state, and cell state of LSTMP. The shape of projection is (T x P), for the cell state which is (T x D), and both LoD is the same with the input.

    +
    Return type:

    tuple

    +
    +

    Examples

    +
    hidden_dim, proj_dim = 512, 256
+fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                         act=None, bias_attr=None)
+proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
+                                         size=hidden_dim * 4,
+                                         proj_size=proj_dim,
+                                         use_peepholes=False,
+                                         is_reverse=True,
+                                         cell_activation="tanh",
+                                         proj_activation="tanh")
+
    +
    +
    + +
    +
    +

    dynamic_gru

    +
    +
    +paddle.v2.fluid.layers.dynamic_gru(input, size, param_attr=None, bias_attr=None, is_reverse=False, gate_activation='sigmoid', candidate_activation='tanh', h_0=None)
    +

    Dynamic GRU Layer

    +

    Refer to Empirical Evaluation of Gated Recurrent Neural Networks on +Sequence Modeling

    +

    The formula is as follows:

    +
    +\[ \begin{align}\begin{aligned}u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)\\r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)\\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)\\h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \tilde{h_t}\end{aligned}\end{align} \]
    +

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) +is the update gate and reset gate activation function and \(sigmoid\) +is usually used for it. \(act_c\) is the activation function for +candidate hidden state and \(tanh\) is usually used for it.

    +

    Note that these \(W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}\) operations on +the input \(x_{t}\) are NOT included in this operator. Users can choose +to use fully-connect layer before GRU layer.

    -
    Parameters:
      -
    • input (Variable) – Input tensor, has predictions.
      • -
    • label (Variable) – Label tensor, has target labels.
      • +
    • input (Variable) – The input of dynamic_gru layer, which supports +variable-time length input sequence. The underlying tensor in this +Variable is a matrix with shape \((T \times 3D)\), where +\(T\) is the total time steps in this mini-batch, \(D\) +is the hidden size.
      • +
    • size (int) – The dimension of the gru cell.
      • +
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable +hidden-hidden weight matrix. Note:

      +
        +
      • The shape of the weight matrix is \((T \times 3D)\), where +\(D\) is the hidden size.
        • +
      • All elements in the weight matrix can be divided into two parts. +The first part are weights of the update gate and reset gate with +shape \((D \times 2D)\), and the second part are weights for +candidate hidden state with shape \((D \times D)\).
        • +
      +
      • +
    • bias_attr (ParamAttr) – The parameter attribute for learnable the +hidden-hidden bias.
      • +
    • is_reverse (bool) – Whether to compute reversed GRU, default +False.
      • +
    • gate_activation (str) – The activation for update gate and reset gate. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “sigmoid”.
      • +
    • activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “tanh”.
    Returns:

    -
    The tensor variable storing the element-wise squared error
    -

    difference of input and label.

    -
    -
    -

    +
    Returns:

    The hidden state of GRU. The shape is (T times D), and lod is the same with the input.
    Return type:

    Variable

    @@ -1484,199 +1509,884 @@ squared error cost.

    Examples

    -
    y = layers.data(name='y', shape=[1], dtype='float32')
-y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
-cost = layers.square_error_cost(input=y_predict, label=y)
+
    hidden_dim = 512
+x = fluid.layers.fc(input=data, size=hidden_dim * 3)
+hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
+
    
+
    
+
    + +
    +
    +

    gru_unit

    +
    +
    +paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')
    +

    GRU unit layer. The equation of a gru step is:

    +
    +
    +\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)\\h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})\end{aligned}\end{align} \]
    +
    +

    The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms +of the equation above, the \(z_t\) is split into 3 parts - +\(xu_t\), \(xr_t\) and \(xm_t\). This means that in order to +implement a full GRU unit operator for an input, a fully +connected layer has to be applied, such that \(z_t = W_{fc}x_t\).

    +

    The terms \(u_t\) and \(r_t\) represent the update and reset gates +of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is +an intermediate candidate hidden output, which is denoted by \(m_t\). +This layer has three outputs \(h_t\), \(dot(r_t, h_{t-1})\) +and concatenation of \(u_t\), \(r_t\) and \(m_t\).

    + +++ + + + + + + + +
    Parameters:
      +
    • input (Variable) – The fc transformed input value of current step.
      • +
    • hidden (Variable) – The hidden value of lstm unit from previous step.
      • +
    • size (integer) – The input dimension value.
      • +
    • weight (ParamAttr) – The weight parameters for gru unit. Default: None
      • +
    • bias (ParamAttr) – The bias parameters for gru unit. Default: None
      • +
    • activation (string) – The activation type for cell (actNode). +Default: ‘tanh’
      • +
    • gate_activation (string) – The activation type for gates (actGate). +Default: ‘sigmoid’
      • +
    +
    Returns:

    The hidden value, reset-hidden value and gate values.

    +
    Return type:

    tuple

    +
    +

    Examples

    +
    # assuming we have x_t_data and prev_hidden of size=10
+x_t = fluid.layers.fc(input=x_t_data, size=30)
+hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
+                                       hidden = prev_hidden)
+
    +
    +
    + +
    +
    +

    linear_chain_crf

    +
    +
    +paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)
    +
    + +
    +
    +

    crf_decoding

    +
    +
    +paddle.v2.fluid.layers.crf_decoding(input, param_attr, label=None)
    +
    + +
    +
    +

    cos_sim

    +
    +
    +paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)
    +

    This function performs the cosine similarity between two tensors +X and Y and returns that as the output.

    +
    + +
    +
    +

    cross_entropy

    +
    +
    +paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)
    +

    Cross Entropy Layer

    +

    This layer computes the cross entropy between input and label. It +supports both standard cross-entropy and soft-label cross-entropy loss +computation.

    +
      +
    1. +
      One-hot cross-entropy:
      +

      soft_label = False, Label[i, 0] indicates the class index for sample i:

      +
      +\[Y[i] = -\log(X[i, Label[i]])\]
      +
      +
      +
      2. +
    2. +
      Soft-label cross-entropy:
      +

      soft_label = True, Label[i, j] indicates the soft label of class j +for sample i:

      +
      +\[Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}\]
      +
      +
      +

      Please make sure that in this case the summation of each row of label +equals one.

      +
      3. +
    3. +
      One-hot cross-entropy with vecterized label:
      +

      As a special case of 2), when each row of ‘label’ has only one +non-zero element which is equal to 1, soft-label cross-entropy degenerates +to a one-hot cross-entropy with one-hot label representation.

      +
      +
      +
      4. +
    + +++ + + + + + + + +
    Parameters:
      +
    • input (Variable|list) – a 2-D tensor with shape [N x D], where N is the +batch size and D is the number of classes. This +input is a probability computed by the previous +operator, which is almost always the result of +a softmax operator.
      • +
    • label (Variable|list) – the ground truth which is a 2-D tensor. When +soft_label is set to False, label is a +tensor<int64> with shape [N x 1]. When +soft_label is set to True, label is a +tensor<float/double> with shape [N x D].
      • +
    • soft_label (bool, via **kwargs) – a flag indicating whether to +interpretate the given labels as soft +labels, default False.
      • +
    +
    Returns:

    A 2-D tensor with shape [N x 1], the cross entropy loss.

    +
    Raises:

    ValueError – 1) the 1st dimension of input and label are not equal. +2) when soft_label == True, and the 2nd dimension of

    +
    +

    input and label are not equal.

    +
    +
      +
    1. when soft_label == False, and the 2nd dimension of +label is not 1.
      2. +
    +
    +

    Examples

    +
    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+
    +
    +
    + +
    +
    +

    square_error_cost

    +
    +
    +paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)
    +

    Square error cost layer

    +

    This layer accepts input predictions and target label and returns the +squared error cost.

    +

    For predictions, \(X\), and target labels, \(Y\), the equation is:

    +
    +\[Out = (X - Y)^2\]
    +

    In the above equation:

    +
    +
      +
    • \(X\): Input predictions, a tensor.
      • +
    • \(Y\): Input labels, a tensor.
      • +
    • \(Out\): Output value, same shape with \(X\).
      • +
    +
    + +++ + + + + + + + +
    Parameters:
      +
    • input (Variable) – Input tensor, has predictions.
      • +
    • label (Variable) – Label tensor, has target labels.
      • +
    +
    Returns:

    +
    The tensor variable storing the element-wise squared error
    +

    difference of input and label.

    +
    +
    +

    +
    Return type:

    Variable

    +
    +

    Examples

    +
    y = layers.data(name='y', shape=[1], dtype='float32')
+y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
+cost = layers.square_error_cost(input=y_predict, label=y)
+
    +
    +
    + +
    +
    +

    accuracy

    +
    +
    +paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)
    +

    This function computes the accuracy using the input and label. +The output is the top_k inputs and their indices.

    +
    + +
    +
    +

    chunk_eval

    +
    +
    +paddle.v2.fluid.layers.chunk_eval(input, label, chunk_scheme, num_chunk_types, excluded_chunk_types=None, **kwargs)
    +

    This function computes and outputs the precision, recall and +F1-score of chunk detection.

    +
    + +
    +
    +

    sequence_conv

    +
    +
    +paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None)
    +

    This function creates the op for sequence_conv, using the inputs and +other convolutional configurations for the filters and stride as given +in the input parameters to the function.

    +
    + +
    +
    +

    conv2d

    +
    +
    +paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=None, padding=None, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None)
    +

    Convlution2D Layer

    +

    The convolution2D layer calculates the output based on the input, filter +and strides, paddings, dilations, groups parameters. Input(Input) and +Output(Output) are in NCHW format. Where N is batch size, C is the number of +channels, H is the height of the feature, and W is the width of the feature. +The details of convolution layer, please refer UFLDL’s convolution, . +If bias attribution and activation type are provided, bias is added to the +output of the convolution, and the corresponding activation function is +applied to the final result.

    +

    For each input \(X\), the equation is:

    +
    +\[Out = \sigma (W \ast X + b)\]
    +

    In the above equation:

    +
      +
    • \(X\): Input value, a tensor with NCHW format.
      • +
    • \(W\): Filter value, a tensor with MCHW format.
      • +
    • \(\ast\): Convolution operation.
      • +
    • \(b\): Bias value, a 2-D tensor with shape [M, 1].
      • +
    • \(\sigma\): Activation function.
      • +
    • +
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      +
      different.
      +
      +
      • +
    +

    Example

    +
      +

    • Input:

      +

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      +

      Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

      +
      • +

    • Output: +Output shape: $(N, C_{out}, H_{out}, W_{out})$

      +
      • +
    +

    Where

    +
    +\[\]
    +

    H_{out}&= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \ +W_{out}&= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

    + +++ + + + + + + + + + +
    Parameters:
      +
    • input (Variable) – The input image with [N, C, H, W] format.
      • +
    • num_filters (int) – The number of filter. It is as same as the output +image channel.
      • +
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square.
      • +
    • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
      • +
    • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding. Default: padding = 0.
      • +
    • groups (int) – The groups number of the Conv2d Layer. According to grouped +convolution in Alex Krizhevsky’s Deep CNN paper: when group=2, +the first half of the filters is only connected to the first half +of the input channels, while the second half of the filters is only +connected to the second half of the input channels. Default: groups=1
      • +
    • param_attr (ParamAttr) – The parameters to the Conv2d Layer. Default: None
      • +
    • bias_attr (ParamAttr) – Bias parameter for the Conv2d layer. Default: None
      • +
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
      • +
    • act (str) – Activation type. Default: None
      • +
    +
    Returns:

    +
    The tensor variable storing the convolution and
    +

    non-linearity activation result.

    +
    +
    +

    +
    Return type:

    Variable

    +
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and +groups mismatch.

    +
    +

    Examples

    +
    data = fluid.layers.data(
+    name='data', shape=[3, 32, 32], dtype='float32')
+conv2d = fluid.layers.conv2d(
+    input=data, num_filters=2, filter_size=3, act="relu")
+
    +
    +
    + +
    +
    +

    sequence_pool

    +
    +
    +paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)
    +

    This function add the operator for sequence pooling. +It pools features of all time-steps of each instance, and is applied +on top of the input using pool_type mentioned in the parameters.

    +

    It supports four pool_type:

    +
      +
    • average: \(Out[i] = \frac{\sum_i X_i}{N}\)
      • +
    • sum: \(Out[i] = \sum_jX_{ij}\)
      • +
    • sqrt: \(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)
      • +
    • max: \(Out[i] = max(X_i)\)
      • +
    +
    x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+
+for different pool_type:
+  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
+  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
+  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
+             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
+  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
+
    +
    + +++ + + + + + +
    Parameters:
      +
    • input (variable) – The input variable which is a LoDTensor.
      • +
    • pool_type (string) – The pooling type of sequence_pool. +It supports average, sum, sqrt and max.
      • +
    +
    Returns:

    The sequence pooling variable which is a Tensor.

    +
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
+sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
+sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
+max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
+
    +
    +
    + +
    +
    +

    pool2d

    +
    +
    +paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=None, pool_padding=None, global_pooling=False, use_cudnn=True, name=None)
    +

    This function adds the operator for pooling in 2 dimensions, using the +pooling configurations mentioned in input parameters.

    +
    + +
    +
    +

    batch_norm

    +
    +
    +paddle.v2.fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', name=None)
    +

    This function helps create an operator to implement +the BatchNorm layer using the configurations from the input parameters.

    +
    + +
    +
    +

    beam_search_decode

    +
    +
    +paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)
    +
    + +
    +
    +

    conv2d_transpose

    +
    +
    +paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
    +

    Convlution2D transpose layer

    +

    The convolution2D transpose layer calculates the output based on the input, +filter, and dilations, strides, paddings. Input(Input) and output(Output) +are in NCHW format. Where N is batch size, C is the number of channels, +H is the height of the feature, and W is the width of the feature. +Parameters(dilations, strides, paddings) are two elements. These two elements +represent height and width, respectively. The details of convolution transpose +layer, please refer to the following explanation and references +therein.

    +

    For each input \(X\), the equation is:

    +
    +\[Out = W \ast X\]
    +

    In the above equation:

    +
      +
    • \(X\): Input value, a tensor with NCHW format.
      • +
    • \(W\): Filter value, a tensor with MCHW format.
      • +
    • \(\ast\) : Convolution transpose operation.
      • +
    • +
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      +
      different.
      +
      +
      • +
    +

    Example

    +
      +

    • Input:

      +

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      +

      Filter shape: $(C_{in}, C_{out}, H_f, W_f)$

      +
      • +

    • Output:

      +

      Output shape: $(N, C_{out}, H_{out}, W_{out})$

      +
      • +
    +

    Where

    +
    +\[\begin{split}H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\ +W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]
    + +++ + + + + + + + + + +
    Parameters:
      +
    • input (Variable) – The input image with [N, C, H, W] format.
      • +
    • num_filters (int) – The number of the filter. It is as same as the output +image channel.
      • +
    • output_size (int|tuple|None) – The output image size. If output size is a +tuple, it must contain two integers, (image_H, image_W). This +parameter only works when filter_size is None.
      • +
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square. None if use output size to +calculate filter_size.
      • +
    • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding. Default: padding = 0.
      • +
    • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
      • +
    • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must +contain two integers, (dilation_H, dilation_W). Otherwise, the +dilation_H = dilation_W = dilation. Default: dilation = 1.
      • +
    • param_attr (ParamAttr) – The parameters to the Conv2d_transpose Layer. +Default: None
      • +
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    Returns:

    The tensor variable storing the convolution transpose result.

    +
    Return type:

    Variable

    +
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and +groups mismatch.

    +
    +

    Examples

    +
    data = fluid.layers.data(
+    name='data', shape=[3, 32, 32], dtype='float32')
+conv2d_transpose = fluid.layers.conv2d_transpose(
+    input=data, num_filters=2, filter_size=3)
+
    +
    +
    + +
    +
    +

    sequence_expand

    +
    +
    +paddle.v2.fluid.layers.sequence_expand(x, y, name=None)
    +

    Sequence Expand Layer. This layer will expand the input variable x +according to LoD information of y. And the following examples will +explain how sequence_expand works:

    +
    * Case 1
+    x is a LoDTensor:
+        x.lod = [[0,       2, 3],
+                 [0, 1,    3, 4]]
+        x.data = [a, b, c, d]
+        x.dims = [4, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0,    2,    4],
+                 [0, 3, 6, 7, 8]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 2-level LoDTensor:
+        out.lod = [[0,                2,    4],
+                   [0,       3,       6, 7, 8]]
+        out.data = [a, a, a, b, b, b, c, d]
+        out.dims = [8, 1]
+
+* Case 2
+    x is a Tensor:
+        x.data = [a, b, c]
+        x.dims = [3, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0, 2, 3, 6]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 1-level LoDTensor:
+        out.lod = [[0,    2, 3,      6]]
+        out.data = [a, a, b, c, c, c]
+        out.dims = [6, 1]
+
    +
    + +++ + + + + + + + +
    Parameters:
      +
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • y (Variable) – The input variable which is a LoDTensor.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    Returns:

    The expanded variable which is a LoDTensor.

    +
    Return type:

    Variable

    +
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[10], dtype='float32')
+y = fluid.layers.data(name='y', shape=[10, 20],
+                 dtype='float32', lod_level=1)
+out = layers.sequence_expand(x=x, y=y)
+
    +
    +
    + +
    +
    +

    lstm_unit

    +
    +
    +paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None, name=None)
    +

    Lstm unit layer. The equation of a lstm step is:

    +
    +
    +\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
    +
    +

    The inputs of lstm unit include \(x_t\), \(h_{t-1}\) and +\(c_{t-1}\). The 2nd dimensions of \(h_{t-1}\) and \(c_{t-1}\) +should be same. The implementation separates the linear transformation and +non-linear transformation apart. Here, we take \(i_t\) as an example. +The linear transformation is applied by calling a fc layer and the +equation is:

    +
    +
    +\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i\]
    +
    +

    The non-linear transformation is applied by calling lstm_unit_op and the +equation is:

    +
    +
    +\[i_t = \sigma(L_{i_t})\]
    +
    +

    This layer has two outputs including \(h_t\) and \(o_t\).

    + +++ + + + + + + + + + +
    Parameters:
      +
    • x_t (Variable) – The input value of current step, a 2-D tensor with shape +M x N, M for batch size and N for input size.
      • +
    • hidden_t_prev (Variable) – The hidden value of lstm unit, a 2-D tensor +with shape M x S, M for batch size and S for size of lstm unit.
      • +
    • cell_t_prev (Variable) – The cell value of lstm unit, a 2-D tensor with +shape M x S, M for batch size and S for size of lstm unit.
      • +
    • forget_bias (float) – The forget bias of lstm unit.
      • +
    • param_attr (ParamAttr) – The attributes of parameter weights, used to set +initializer, name etc.
      • +
    • bias_attr (ParamAttr) – The attributes of bias weights, if not False, +bias weights will be created and be set to default value.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    Returns:

    The hidden value and cell value of lstm unit.

    +
    Return type:

    tuple

    +
    Raises:

    ValueError – The ranks of x_t, hidden_t_prev and cell_t_prev +not be 2 or the 1st dimensions of x_t, hidden_t_prev +and cell_t_prev not be the same or the 2nd dimensions of +hidden_t_prev and cell_t_prev not be the same.

    +
    +

    Examples

    +
    x_t = fluid.layers.fc(input=x_t_data, size=10)
+prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
+prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
+                                       hidden_t_prev=prev_hidden,
+                                       cell_t_prev=prev_cell)
+
    +
    +
    + +
    +
    +

    reduce_sum

    +
    +
    +paddle.v2.fluid.layers.reduce_sum(input, dim=None, keep_dim=False, name=None)
    +

    Computes the sum of tensor elements over the given dimension.

    + +++ + + + + + + + +
    Parameters:
      +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the sum is performed. If +None, sum all elements of input and return a +Tensor variable with a single element, otherwise must be in the +range \([-rank(input), rank(input))\). If \(dim < 0\), +the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    Returns:

    The reduced Tensor variable.

    +
    Return type:

    Variable

    +
    +

    Examples

    +
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_sum(x)  # [3.5]
+fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
+fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
+fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
+
    +
    +
    + +
    +
    +

    reduce_mean

    +
    +
    +paddle.v2.fluid.layers.reduce_mean(input, dim=None, keep_dim=False, name=None)
    +

    Computes the mean of tensor elements over the given dimension.

    + +++ + + + + + + + +
    Parameters:
      +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the mean is computed. If +None, compute the mean over all elements of input +and return a Tensor variable with a single element, otherwise +must be in the range \([-rank(input), rank(input))\). If +\(dim < 0\), the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    Returns:

    The reduced Tensor variable.

    +
    Return type:

    Variable

    +
    +

    Examples

    +
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_mean(x)  # [0.4375]
+fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
+fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
+fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
    -
    -

    accuracy

    -
    -
    -paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)
    -

    This function computes the accuracy using the input and label. -The output is the top_k inputs and their indices.

    -
    - -
    -
    -

    sequence_conv

    -
    -
    -paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None)
    -

    This function creates the op for sequence_conv, using the inputs and -other convolutional configurations for the filters and stride as given -in the input parameters to the function.

    -
    - -
    -
    -

    conv2d

    +
    +

    reduce_max

    -paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=None, padding=None, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None)
    -

    Convlution2D Layer

    -

    The convolution2D layer calculates the output based on the input, filter -and strides, paddings, dilations, groups parameters. Input(Input) and -Output(Output) are in NCHW format. Where N is batch size, C is the number of -channels, H is the height of the feature, and W is the width of the feature. -The details of convolution layer, please refer UFLDL’s convolution, . -If bias attribution and activation type are provided, bias is added to the -output of the convolution, and the corresponding activation function is -applied to the final result.

    -

    For each input \(X\), the equation is:

    -
    -\[Out = \sigma (W \ast X + b)\]
    -

    In the above equation:

    -
      -
    • \(X\): Input value, a tensor with NCHW format.
      • -
    • \(W\): Filter value, a tensor with MCHW format.
      • -
    • \(\ast\): Convolution operation.
      • -
    • \(b\): Bias value, a 2-D tensor with shape [M, 1].
      • -
    • \(\sigma\): Activation function.
      • -
    • -
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      -
      different.
      -
      -
      • -
    -

    Example

    -
      -

    • Input:

      -

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      -

      Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

      -
      • -

    • Output: -Output shape: $(N, C_{out}, H_{out}, W_{out})$

      -
      • -
    -

    Where

    -
    -\[\]
    -

    H_{out}&= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \ -W_{out}&= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

    +paddle.v2.fluid.layers.reduce_max(input, dim=None, keep_dim=False, name=None) +

    Computes the maximum of tensor elements over the given dimension.

    - - - -
    Parameters:
      -
    • input (Variable) – The input image with [N, C, H, W] format.
      • -
    • num_filters (int) – The number of filter. It is as same as the output -image channel.
      • -
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, -it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square.
      • -
    • stride (int|tuple) – The stride size. If stride is a tuple, it must -contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride. Default: stride = 1.
      • -
    • padding (int|tuple) – The padding size. If padding is a tuple, it must -contain two integers, (padding_H, padding_W). Otherwise, the -padding_H = padding_W = padding. Default: padding = 0.
      • -
    • groups (int) – The groups number of the Conv2d Layer. According to grouped -convolution in Alex Krizhevsky’s Deep CNN paper: when group=2, -the first half of the filters is only connected to the first half -of the input channels, while the second half of the filters is only -connected to the second half of the input channels. Default: groups=1
      • -
    • param_attr (ParamAttr) – The parameters to the Conv2d Layer. Default: None
      • -
    • bias_attr (ParamAttr) – Bias parameter for the Conv2d layer. Default: None
      • -
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn -library is installed. Default: True
      • -
    • act (str) – Activation type. Default: None
      • +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the maximum is computed. +If None, compute the maximum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    Returns:

    -
    The tensor variable storing the convolution and
    -

    non-linearity activation result.

    -
    -
    -

    -
    Return type:

    Variable

    +
    Returns:

    The reduced Tensor variable.
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and -groups mismatch.

    +
    Return type:

    Variable

    Examples

    -
    data = fluid.layers.data(
-    name='data', shape=[3, 32, 32], dtype='float32')
-conv2d = fluid.layers.conv2d(
-    input=data, num_filters=2, filter_size=3, act="relu")
+
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_max(x)  # [0.9]
+fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
+fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
+fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
    -
    -

    sequence_pool

    +
    +

    reduce_min

    -paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)
    -

    This function add the operator for sequence pooling. -It pools features of all time-steps of each instance, and is applied -on top of the input using pool_type mentioned in the parameters.

    -

    It supports four pool_type:

    -
      -
    • average: \(Out[i] = \frac{\sum_i X_i}{N}\)
      • -
    • sum: \(Out[i] = \sum_jX_{ij}\)
      • -
    • sqrt: \(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)
      • -
    • max: \(Out[i] = max(X_i)\)
      • -
    -
    x is a 1-level LoDTensor:
-  x.lod = [[0, 2, 5, 7]]
-  x.data = [1, 3, 2, 4, 6, 5, 1]
-  x.dims = [7, 1]
-
-then output is a Tensor:
-  out.dim = [3, 1]
-  with condition len(x.lod[-1]) - 1 == out.dims[0]
-
-for different pool_type:
-  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
-  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
-  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
-             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
-  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
-
    -
    +paddle.v2.fluid.layers.reduce_min(input, dim=None, keep_dim=False, name=None) +

    Computes the minimum of tensor elements over the given dimension.

    - + +
    Parameters:
      -
    • input (variable) – The input variable which is a LoDTensor.
      • -
    • pool_type (string) – The pooling type of sequence_pool. -It supports average, sum, sqrt and max.
      • +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the minimum is computed. +If None, compute the minimum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    Returns:

    The sequence pooling variable which is a Tensor.

    +
    Returns:

    The reduced Tensor variable.

    +
    Return type:

    Variable

    Examples

    -
    x = fluid.layers.data(name='x', shape=[7, 1],
-                 dtype='float32', lod_level=1)
-avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
-sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
-sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
-max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
+
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_min(x)  # [0.1]
+fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
+fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
+fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
    -

    sequence_first_step

    +

    sequence_first_step

    paddle.v2.fluid.layers.sequence_first_step(input, **kwargs)
    @@ -1712,7 +2422,7 @@ then output is a Tensor:
    -

    sequence_last_step

    +

    sequence_last_step

    paddle.v2.fluid.layers.sequence_last_step(input, **kwargs)
    @@ -1733,95 +2443,47 @@ then output is a Tensor: Parameters:input (variable) – The input variable which is a LoDTensor. - -Returns:The sequence’s last step variable which is a Tensor. - - - -

    Examples

    -
    x = fluid.layers.data(name='x', shape=[7, 1],
-                 dtype='float32', lod_level=1)
-x_last_step = fluid.layers.sequence_last_step(input=x)
-
    -
    -
    - -
    -
    -

    pool2d

    -
    -
    -paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=None, pool_padding=None, global_pooling=False, use_cudnn=True, name=None)
    -

    This function adds the operator for pooling in 2 dimensions, using the -pooling configurations mentioned in input parameters.

    -
    - -
    -
    -

    batch_norm

    -
    -
    -paddle.v2.fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', name=None)
    -

    This function helps create an operator to implement -the BatchNorm layer using the configurations from the input parameters.

    -
    - -
    -
    -

    beam_search_decode

    -
    -
    -paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)
    -
    - -
    -
    -

    lod_rank_table

    -
    -
    -paddle.v2.fluid.layers.lod_rank_table(x, level=0)
    -

    LoD Rank Table Operator. Given an input variable x and a level number -of LoD, this layer creates a LodRankTable object. A LoDRankTable object -contains a list of bi-element tuples. Each tuple consists of an index and -a length, both of which are int type. Refering to specified level of LoD, -the index is the sequence index number and the length representes the -sequence length. Please note that the list is ranked in descending order by -the length. The following is an example:

    -
    -
    x is a LoDTensor:
-    x.lod = [[0,                2, 3],
-             [0,             5, 6, 7]]
-    x.data = [a, b, c, d, e, f, g]
-
-1. set level to 0:
-    Create lod rank table:
-        lod_rank_table_obj = lod_rank_table(x, level=0)
-
-    Get:
-        lod_rank_table_obj.items() = [(0, 2), (1, 1)]
-
-2. set level to 1:
-    Create lod rank table:
-        lod_rank_table_obj = lod_rank_table(x, level=1)
-
-    Get:
-        lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]
+
+Returns:The sequence’s last step variable which is a Tensor.
+
+
+
+
    Examples
    
+
    x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+x_last_step = fluid.layers.sequence_last_step(input=x)
 
    
 
    
-
    +
    + +
    +
    +

    dropout

    +
    +
    +paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=None, **kwargs)
    +

    Computes dropout.

    +

    Drop or keep each element of x independently. Dropout is a regularization +technique for reducing overfitting by preventing neuron co-adaption during +training. The dropout operator randomly set (according to the given dropout +probability) the outputs of some units to zero, while others are remain +unchanged.

    -
    Parameters:
      -
    • x (Variable) – Input variable, a LoDTensor based which to create the lod -rank table.
      • -
    • level (int) – Specify the LoD level, on which to create the lod rank -table.
      • +
    • x (variable) – The input tensor.
      • +
    • dropout_prob (float) – Probability of setting units to zero.
      • +
    • is_test (bool) – A flag indicating whether it is in test phrase or not.
      • +
    • seed (int) – A Python integer used to create random seeds. If this +parameter is set to None, a random seed is used. +NOTE: If an integer seed is given, always the same output +units will be dropped. DO NOT use a fixed seed in training.
    Returns:

    The created LoDRankTable object.

    +
    Returns:

    A tensor variable.
    Return type:

    Variable

    @@ -1830,74 +2492,116 @@ table.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10],
-                dtype='float32', lod_level=1)
-out = layers.lod_rank_table(x=x, level=0)
+
    x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
    -
    -

    max_sequence_len

    +
    +

    split

    -paddle.v2.fluid.layers.max_sequence_len(rank_table)
    -

    Max Sequence Len Operator. Given a LoDRankTable object, this layer -returns the max length of a batch of sequences. In fact, a LoDRankTable -object contains a list of tuples(<sequence index, sequence length>) and -the list is already sorted by sequence length in descending order, so the -operator just returns the sequence length of the first tuple element.

    +paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None) +

    Split the input tensor into multiple sub-tensors.

    - + - + - +
    Parameters:rank_table (Variable) – Input variable which is a LoDRankTable object.
    Parameters:
      +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • num_or_sections (int|list) – If num_or_sections is an integer, +then the integer indicates the number of equal sized sub-tensors +that the tensor will be divided into. If num_or_sections +is a list of integers, the length of list indicates the number of +sub-tensors and the integers indicate the sizes of sub-tensors’ +dim dimension orderly.
      • +
    • dim (int) – The dimension along which to split. If \(dim < 0\), the +dimension to split along is \(rank(input) + dim\).
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    Returns:The max length of sequence.
    Returns:

    The list of segmented tensor variables.

    +
    Return type:Variable
    Return type:

    List

    +

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10],
-                dtype='float32', lod_level=1)
-rank_table = layers.lod_rank_table(x=x, level=0)
-max_seq_len = layers.max_sequence_len(rank_table)
+
    # x is a Tensor variable with shape [3, 9, 5]:
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
+x0.shape  # [3, 3, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 3, 5]
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
+x0.shape  # [3, 2, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 4, 5]
    -
    -

    topk

    +
    +

    ctc_greedy_decoder

    -paddle.v2.fluid.layers.topk(input, k)
    -

    topk

    -

    This function performs the operation that selects the k entries in the input -vector and outputs their values and indices as vectors. Thus topk_out[j] is -the j-th largest entry in input, and its index is topk_indices[j]

    +paddle.v2.fluid.layers.ctc_greedy_decoder(input, blank, name=None) +

    This op is used to decode sequences by greedy policy by below steps: +1. Get the indexes of max value for each row in input. a.k.a.

    +
    +
    numpy.argmax(input, axis=0).
    +
      +
    1. For each sequence in result of step1, merge repeated tokens between two +blanks and delete all blanks.
      2. +
    +

    A simple example as below:

    +
    Given:
+
+input.data = [[0.6, 0.1, 0.3, 0.1],
+              [0.3, 0.2, 0.4, 0.1],
+              [0.1, 0.5, 0.1, 0.3],
+              [0.5, 0.1, 0.3, 0.1],
+
+              [0.5, 0.1, 0.3, 0.1],
+              [0.2, 0.2, 0.2, 0.4],
+              [0.2, 0.2, 0.1, 0.5],
+              [0.5, 0.1, 0.3, 0.1]]
+
+input.lod = [[0, 4, 8]]
+
+Then:
+
+output.data = [[2],
+               [1],
+               [3]]
+
+output.lod = [[0, 2, 3]]
+
    +
    -
    Parameters:
      -
    • input (Variable|list) – The input tensor that has all the data.
      • -
    • k (int) – The number of top elements that the function will pick.
      • +
    • input (Variable) – (LoDTensor<float>), the probabilities of +variable-length sequences, which is a 2-D Tensor with +LoD information. It’s shape is [Lp, num_classes + 1], +where Lp is the sum of all input sequences’ length and +num_classes is the true number of classes. (not +including the blank label).
      • +
    • blank (int) – the blank label index of Connectionist Temporal +Classification (CTC) loss, which is in thehalf-opened +interval [0, num_classes + 1).
    Returns:

    -
    The variable of type array that contains the k largest entries
    -

    from input.

    -
    -
    Variable: The variable of type array that contains the indices of k
    -

    largest entries from input.

    -
    -
    -

    +
    Returns:

    CTC greedy decode result.
    Return type:

    Variable

    @@ -1906,38 +2610,50 @@ the j-th largest entry in input, and its index is topk_indices[j]

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10])
-k = 5
-array = fluid.layers.topk(x, k)
+
    x = fluid.layers.data(name='x', shape=[8], dtype='float32')
+
+cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
    -
    -

    lod_tensor_to_array

    +
    +

    edit_distance

    -paddle.v2.fluid.layers.lod_tensor_to_array(x, table)
    -

    Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.

    +paddle.v2.fluid.layers.edit_distance(input, label, normalized=False, ignored_tokens=None, name=None) +

    EditDistance operator computes the edit distances between a batch of +hypothesis strings and their references. Edit distance, also called +Levenshtein distance, measures how dissimilar two strings are by counting +the minimum number of operations to transform one string into anthor. +Here the operations include insertion, deletion, and substitution.

    +

    For example, given hypothesis string A = “kitten” and reference +B = “sitting”, the edit distance is 3 for A will be transformed into B +at least after two substitutions and one insertion:

    +

    “kitten” -> “sitten” -> “sittin” -> “sitting”

    +

    Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with +the total number denoted by batch_size, and the separation is specified +by the LoD information. And the batch_size reference strings are arranged +in order in the same way in the LoDTensor Input(Refs).

    +

    Output(Out) contains the batch_size results and each stands for the edit +distance for a pair of strings respectively. If Attr(normalized) is true, +the edit distance will be divided by the length of reference string.

    -
    Parameters:
      -
    • x (Variable|list) – The LOD tensor to be converted to a LOD tensor array.
      • -
    • table (ParamAttr|list) – The variable that stores the level of lod -which is ordered by sequence length in -descending order.
      • +
    • input (Variable) – The indices for hypothesis strings.
      • +
    • label (Variable) – The indices for reference strings.
      • +
    • normalized (bool) – Indicated whether to normalize the edit distance by +the length of reference string.
      • +
    • ignored_tokens (list of int) – Tokens that should be removed before +calculating edit distance.
    Returns:

    -
    The variable of type array that has been converted from a
    -

    tensor.

    -
    -
    -

    +
    Returns:

    sequence-to-sequence edit distance in shape [batch_size, 1].
    Return type:

    Variable

    @@ -1946,38 +2662,42 @@ descending order.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10])
-table = fluid.layers.lod_rank_table(x, level=0)
-array = fluid.layers.lod_tensor_to_array(x, table)
+
    x = fluid.layers.data(name='x', shape=[8], dtype='float32')
+y = fluid.layers.data(name='y', shape=[7], dtype='float32')
+
+cost = fluid.layers.edit_distance(input=x,label=y)
    -
    -

    array_to_lod_tensor

    +
    +

    l2_normalize

    -paddle.v2.fluid.layers.array_to_lod_tensor(x, table)
    -

    Convert a LoD_Tensor_Aarry to an LoDTensor.

    +paddle.v2.fluid.layers.l2_normalize(x, axis, epsilon=1e-12, name=None) +

    L2 normalize Layer

    +

    The l2 normalize layer normalizes x along dimension axis using an L2 +norm. For a 1-D tensor (dim is fixed to 0), this layer computes

    +

    output = x / sqrt(max(sum(x**2), epsilon))

    +

    For x with more dimensions, this layer independently normalizes each 1-D +slice along dimension axis.

    -
    Parameters:
      -
    • x (Variable|list) – The lod tensor array to be converted to a tensor.
      • -
    • table (ParamAttr|list) – The variable that stores the level of lod -which is ordered by sequence length in -descending order.
      • +
    • x (Variable|list) – The input tensor to l2_normalize layer.
      • +
    • axis (int) – Dimension along which to normalize the input.
      • +
    • epsilon (float) – A lower bound value for x‘s l2 norm. sqrt(epsilon) will +be used as the divisor if the l2 norm of x is less than +sqrt(epsilon).
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    Returns:

    -
    The variable of type tensor that has been converted
    -

    from an array.

    -
    -
    -

    +
    Returns:

    The output tensor variable.
    Return type:

    Variable

    @@ -1986,37 +2706,59 @@ descending order.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10])
-table = fluid.layers.lod_rank_table(x, level=0)
-array = fluid.layers.lod_tensor_to_array(x, table)
-lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
+
    data = fluid.layers.data(name="data",
+                         shape=(3, 17, 13),
+                         dtype="float32")
+normed = fluid.layers.l2_normalize(x=data, axis=1)
    -
    -

    fill_constant

    +
    +

    matmul

    -paddle.v2.fluid.layers.fill_constant(shape, dtype, value, force_cpu=False, out=None)
    -

    fill_constant

    -

    This function creates a tensor with specified shape and dtype, and -initializes it with a constant specifed by value.

    -

    The attribute stop_gradient of the created tensor is set to True.

    +paddle.v2.fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, name=None) +

    Applies matrix multiplication to two tensors.

    +

    Currently, the input tensors’ rank can be any, but when the rank of any +inputs is bigger than 3, this two inputs’ rank should be equal.

    +

    The actual behavior depends on the shapes of \(x\), \(y\) and the +flag values of transpose_x, transpose_y. Specifically:

    +
      +
    • If a transpose flag is specified, the last two dimensions of the tensor +are transposed. If the tensor is rank-1 of shape \([D]\), then for +\(x\) it is treated as \([1, D]\) in nontransposed form and as +\([D, 1]\) in transposed form, whereas for \(y\) it is the +opposite: It is treated as \([D, 1]\) in nontransposed form and as +\([1, D]\) in transposed form.
      • +
    • After transpose, the two tensors are 2-D or n-D and matrix multiplication +performs in the following way.
        +
      • If both are 2-D, they are multiplied like conventional matrices.
        • +
      • If either is n-D, it is treated as a stack of matrices residing in the +last two dimensions and a batched matrix multiply supporting broadcast +applies on the two tensors.
        • +
      +
      • +
    +

    Also note that if the raw tensor \(x\) or \(y\) is rank-1 and +nontransposed, the prepended or appended dimension \(1\) will be +removed after matrix multiplication.

    -
    Parameters:
      -
    • shape (tuple|list|None) – Shape of the output tensor.
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of the output tensor.
      • -
    • value (float) – The constant value used to initialize the output tensor.
      • -
    • out (Variable) – The output tensor.
      • +
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • y (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • transpose_x (bool) – Whether to transpose \(x\) before multiplication.
      • +
    • transpose_y (bool) – Whether to transpose \(y\) before multiplication.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    Returns:

    The tensor variable storing the output.

    +
    Returns:

    The product Tensor variable.
    Return type:

    Variable

    @@ -2025,37 +2767,70 @@ initializes it with a constant specifed by value.

    Examples

    -
    data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
+
    # Examples to clarify shapes of the inputs and output
+# x: [B, ..., M, K], y: [B, ..., K, N]
+fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
+
+# x: [B, M, K], y: [B, K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+
+# x: [B, M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+
+# x: [M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [M, N]
+
+# x: [B, M, K], y: [K]
+fluid.layers.matmul(x, y)  # out: [B, M]
+
+# x: [K], y: [K]
+fluid.layers.matmul(x, y)  # out: [1]
+
+# x: [M], y: [N]
+fluid.layers.matmul(x, y, True, True)  # out: [M, N]
    -
    -

    fill_constant_batch_size_like

    +
    +

    warpctc

    -paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0)
    -

    fill_constant_batch_size_like

    -

    This function creates a tensor of specified shape, dtype and batch size, -and initializes this with a constant supplied in value. The batch size is -obtained from the input tensor.

    -

    It also sets stop_gradient to True.

    +paddle.v2.fluid.layers.warpctc(input, label, blank=0, norm_by_times=False, **kwargs) +

    An operator integrating the open source Warp-CTC library +(https://github.com/baidu-research/warp-ctc) +to compute Connectionist Temporal Classification (CTC) loss. +It can be aliased as softmax with CTC, since a native softmax activation is +interated to the Warp-CTC library, to to normlize values for each row of the +input tensor.

    -
    Parameters:
      -
    • input (Variable) – Tensor whose dimensions will be used to get batch size
      • -
    • shape (tuple|list|None) – Shape of output tensor
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • -
    • value (float) – Constant value to initialize the output tensor
      • -
    • input_dim_idx (int) – Index of input’s batch size dimension
      • -
    • output_dim_idx (int) – Index of output’s batch size dimension
      • +
    • input (Variable) – (LodTensor, default: LoDTensor<float>), +the unscaled probabilities of variable-length sequences, +which is a 2-D Tensor with LoD information. +It’s shape is [Lp, num_classes + 1], where Lp is the sum of all input +sequences’ length and num_classes is the true number of classes. +(not including the blank label).
      • +
    • label (Variable) – (LodTensor, default: LoDTensor<int>), the ground truth +of variable-length sequence, which is a 2-D Tensor with LoD +information. It is of the shape [Lg, 1], where Lg is th sum of +all labels’ length.
      • +
    • blank – (int, default: 0), the blank label index of Connectionist +Temporal Classification (CTC) loss, which is in the +half-opened interval [0, num_classes + 1).
      • +
    • norm_by_times – (bool, default: false), whether to normalize
      • +
    • gradients by the number of time-step, which is also the (the) –
      • +
    • length. There is no need to normalize the gradients (sequence's) –
      • +
    • warpctc layer was follewed by a mean_op. (if) –
    Returns:

    The tensor variable storing the output

    +
    Returns:

    The Connectionist Temporal Classification (CTC) loss, +which is a 2-D Tensor of the shape [batch_size, 1].
    Return type:

    Variable

    @@ -2064,33 +2839,49 @@ obtained from the input tensor.

    Examples

    -
    data = fluid.layers.fill_constant_batch_size_like(
-    input=like, shape=[1], value=0, dtype='int64')
-
    -
    -
    -

    ones

    +
    +

    sequence_reshape

    -paddle.v2.fluid.layers.ones(shape, dtype)
    -

    ones

    -

    This function creates a tensor of specified shape and -dtype, and initializes this with 1.

    -

    It also sets stop_gradient to True.

    +paddle.v2.fluid.layers.sequence_reshape(input, new_dim) +

    Sequence Reshape Layer

    +

    This layer will rearrange the input sequences. The new dimension is set by +user. Length of each sequence is computed according to original length, +original dimension and new dimension. The following example will help to +illustrate the function of this layer:

    +
    x is a LoDTensor:
+    x.lod  = [[0, 2, 6]]
+    x.data = [[1, 2], [3, 4],
+              [5, 6], [7, 8], [9, 10], [11, 12]]
+    x.dims = [6, 2]
+
+set new_dim = 4
+
+then out is a LoDTensor:
+    out.lod  = [[0, 1, 3]]
+    out.data = [[1, 2, 3, 4],
+                [5, 6, 7, 8], [9, 10, 11, 12]]
+    out.dims = [3, 4]
+
    +
    +

    Currently, only 1-level LoDTensor is supported and please make sure +(original length * original dimension) can be divided by new dimension with +no remainder for each sequence.

    -
    Parameters:
      -
    • shape (tuple|list|None) – Shape of output tensor
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • +
    • input (Variable) – (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor +with shape being [N, M] where M for dimension.
      • +
    • new_dim (int) – New dimension which the input LoDTensor is reshaped to.
    Returns:

    The tensor variable storing the output

    +
    Returns:

    Reshaped LoDTensor according to new dimension.
    Return type:

    Variable

    @@ -2099,32 +2890,34 @@ obtained from the input tensor.

    Examples

    -
    data = fluid.layers.ones(shape=[1], dtype='int64')
+
    x = fluid.layers.data(name='x', shape=[5, 20],
+                  dtype='float32', lod_level=1)
+x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
    -
    -

    zeros

    +
    +

    transpose

    -paddle.v2.fluid.layers.zeros(shape, dtype)
    -

    zeros

    -

    This function creates a tensor of specified shape and -dtype, and initializes this with 0.

    -

    It also sets stop_gradient to True.

    +paddle.v2.fluid.layers.transpose(x, perm, name=None) +

    transpose Layer

    +

    Permute the dimensions of input according to perm.

    +

    The i-th dimension of the returned tensor will correspond to the +perm[i]-th dimension of input.

    -
    Parameters:
      -
    • shape (tuple|list|None) – Shape of output tensor
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • +
    • input (Variable) – (Tensor), A Tensor.
      • +
    • perm (list) – A permutation of the dimensions of input.
    Returns:

    The tensor variable storing the output

    +
    Returns:

    A transposed Tensor.
    Return type:

    Variable

    @@ -2133,134 +2926,249 @@ obtained from the input tensor.

    Examples

    -
    data = fluid.layers.zeros(shape=[1], dtype='int64')
+
    x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
+x_transposed = layers.transpose(x, perm=[1, 0, 2])
    -
    -

    increment

    +
    +

    im2sequence

    -paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True)
    -

    This function performs an operation that increments each value in the -input \(x\) by an amount: \(value\) as mentioned in the input -parameter. This operation is performed in-place by default.

    +paddle.v2.fluid.layers.im2sequence(input, filter_size=1, stride=1, padding=0, name=None) +

    Extracts image patches from the input tensor to form a tensor of shape +{input.batch_size * output_height * output_width, filter_size_H * +filter_size_W * input.channels} which is similar with im2col. +This op use filter / kernel to scan images and convert these images to +sequences. After expanding, the number of time step are +output_height * output_width for an image, in which output_height and +output_width are calculated by below equation:

    +
    +\[output\_size = 1 + (2 * padding + img\_size - block\_size + stride - 1) / stride\]
    +

    And the dimension of each time step is block_y * block_x * input.channels.

    - -
    Parameters:
      -
    • x (Variable|list) – The tensor that has the input values.
      • -
    • value (float) – The amount by which the values should be incremented.
      • -
    • in_place (bool) – If the increment should be performed in-place.
      • +
    • input (Variable) – The input should be a tensor in NCHW format.
      • +
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square.
      • +
    • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
      • +
    • padding (int|tuple) – The padding size. If padding is a tuple, it can +contain two integers like (padding_H, padding_W) which means +padding_up = padding_down = padding_H and +padding_left = padding_right = padding_W. Or it can use +(padding_up, padding_left, padding_down, padding_right) to indicate +paddings of four direction. Otherwise, a scalar padding means +padding_up = padding_down = padding_left = padding_right = padding +Default: padding = 0.
      • +
    • name (int) – The name of this layer. It is optional.
    Returns:

    -
    The tensor variable storing the transformation of
    -

    element-wise increment of each value in the input.

    -
    -
    -

    +
    Returns:

    The output is a LoDTensor with shape +{input.batch_size * output_height * output_width, +filter_size_H * filter_size_W * input.channels}. +If we regard output as a matrix, each row of this matrix is +a step of a sequence.
    Return type:

    Variable

    +
    Return type:

    output
    -

    Examples

    -
    data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
-data = fluid.layers.increment(x=data, value=3.0, in_place=True)
+
    Examples:
    
+
    As an example:
    
+
    
+
    Given:
+
+x = [[[[ 6.  2.  1.]
+       [ 8.  3.  5.]
+       [ 0.  2.  6.]]
+
+      [[ 2.  4.  4.]
+       [ 6.  3.  0.]
+       [ 6.  4.  7.]]]
+
+     [[[ 6.  7.  1.]
+       [ 5.  7.  9.]
+       [ 2.  4.  8.]]
+
+      [[ 1.  2.  1.]
+       [ 1.  3.  5.]
+       [ 9.  0.  8.]]]]
+
+x.dims = {2, 2, 3, 3}
+
+And:
+
+filter = [2, 2]
+stride = [1, 1]
+padding = [0, 0]
+
+Then:
+
+output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
+               [ 2.  1.  3.  5.  4.  4.  3.  0.]
+               [ 8.  3.  0.  2.  6.  3.  6.  4.]
+               [ 3.  5.  2.  6.  3.  0.  4.  7.]
+               [ 6.  7.  5.  7.  1.  2.  1.  3.]
+               [ 7.  1.  7.  9.  2.  1.  3.  5.]
+               [ 5.  7.  2.  4.  1.  3.  9.  0.]
+               [ 7.  9.  4.  8.  3.  5.  0.  8.]]
+
+output.dims = {8, 9}
+
+output.lod = [[0, 4, 8]]
+
    
+
    
+
    The simple usage is:
    
+
    output = fluid.layers.im2sequence(
+    input=layer, stride=[1, 1], filter_size=[2, 2])
 
    
 
    
+
    -
    -

    array_write

    +
    +

    nce

    -paddle.v2.fluid.layers.array_write(x, i, array=None)
    -

    This function writes the given input variable to the specified position -indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the -output LOD_TENSOR_ARRAY is not given(None), a new one will be created and -returned.

    +paddle.v2.fluid.layers.nce(input, label, num_total_classes, sample_weight=None, param_attr=None, bias_attr=None, num_neg_samples=None) +

    Compute and return the noise-contrastive estimation training loss. +See [Noise-contrastive estimation: A new estimation principle for unnormalized statistical models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf). +By default this operator uses a uniform distribution for sampling.

    - - -
    Parameters:
      -
    • x (Variable|list) – The input tensor from which the data will be read.
      • -
    • i (Variable|list) – The index of the output LOD_TENSOR_ARRAY, pointing to -the position to which the input tensor will be -written.
      • -
    • array (Variable|list) – The output LOD_TENSOR_ARRAY to which the input -tensor will be written. If this parameter is -NONE, a new LOD_TENSOR_ARRAY will be created and -returned.
      • +
    • input – (Tensor) A tensor of shape [batch_size, dim]. +Duplicable: False Optional: False
      • +
    • label – (Tensor) A tensor of shape [batch_size, num_true_class]. ‘num_true_class’ is the number of target classes in each sample.The number of target classes per sample should be same. If you have a variable number of target classes, you can pad them out to a constant number by either repeating them or by padding with an otherwise unused class.) +Duplicable: False Optional: False
      • +
    • weight – (Tensor) A tensor of shape [num_class, dim]. ‘num_class’ is the total number of class. +Duplicable: False Optional: False
      • +
    • bias – (Tensor) A tensor of shape [num_class, 1]. ‘num_class’ is the total number of class. It is a dispensable input. +Duplicable: False Optional: True
      • +
    • sample_weight – (Tensor) A tensor of shape [batch_size, 1] storing a weight for each sample. And it is a dispensable input. The default value of sample is 1. +Duplicable: False Optional: True
      • +
    • num_total_classes (INT) – Total number of classes in all samples.
      • +
    • num_neg_samples (INT) – The number of negative classes. The default value is 10.
      • +
    • custom_neg_classes (INTS) – This attribute only be used in unitest. Classes in this list wiil be used as negative classes for every samples. Under normal conditions, user should avoid setting this attribute.
    Returns:

    The output LOD_TENSOR_ARRAY where the input tensor is written.

    -
    Return type:

    Variable

    +
    Returns:

    (Tensor) A tensor of shape [batch_size, 1]. Cost of samples.
    -

    Examples

    -
    -

    create_array

    + +
    +

    row_conv

    +
    +
    +paddle.v2.fluid.layers.row_conv(input, future_context_size, param_attr=None, act=None)
    +

    Row Conv Operator. This layer will apply lookahead convolution to +input. The input variable should be a 2D LoDTensor with shape [T, D]. +Parameters with shape [future_context_size + 1, D] will be created. The math +equation of row convolution is as follows:

    +
    +\[Out_{i} = \sum_{j = i} ^ {i + \tau} X_{j} \odot W_{i - j}\]
    +

    In the above equation:

    +
      +
    • \(Out_{i}\): The i-th row of output variable with shape [1, D].
      • +
    • \(\tau\): Future context size.
      • +
    • \(X_{j}\): The j-th row of input variable with shape [1, D].
      • +
    • \(W_{i-j}\): The (i-j)-th row of parameters with shape [1, D].
      • +
    +

    More details about row_conv please refer to the paper (http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and +the design document (https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).

    - + - + - +
    Parameters:dtype (int|float) – The data type of the elements in the array.
    Parameters:
      +
    • input (Variable) – Input variable, a 2D LoDTensor with shape [T, D].
      • +
    • future_context_size (int) – Future context size. Please note, the shape +of convolution kernel is [future_context_size + 1, D].
      • +
    • param_attr (ParamAttr) – Attributes of parameters, including +name, initializer etc.
      • +
    • act (str) – Non-linear activation to be applied to output variable.
      • +
    +
    Returns:The tensor variable storing the elements of data type.
    Returns:

    The output tensor with same shape as input tensor.

    +
    Return type:Variable
    Return type:

    Variable

    +

    Examples

    -
    data = fluid.layers.create_array(dtype='float32')
+
    x = fluid.layers.data(name='x', shape=[16],
+                dtype='float32', lod_level=1)
+out = fluid.layers.row_conv(input=x, future_context_size=2)
    -
    -

    less_than

    +
    +

    multiplex

    -paddle.v2.fluid.layers.less_than(x, y, cond=None, **ignored)
    -

    Less than

    -

    This layer returns the truth value of \(x < y\) elementwise.

    +paddle.v2.fluid.layers.multiplex(inputs, index) +

    Multiplex Layer

    +

    Referring to the given index variable, this layer selects rows from the +input variables to construct a multiplex variable. Assuming that there are +\(m\) input variables and \(I_i\) represents the i-th input +variable and \(i\) is in [0, \(m\)). All input variables are +tensors with same shape [\(d_0\), \(d_1\), ..., \(d_R\)]. +Please note that rank of the input tensor should be at least 2. Each input +variable will be treated as a 2-D matrix with shape [\(M\), \(N\)] +where \(M\) for \(d_0\) and \(N\) for \(d_1\) * \(d_2\) +* ... * \(d_R\). Let \(I_i[j]\) be the j-th row of the i-th input +variable. The given index variable should be a 2-D tensor with shape +[\(M\), 1]. Let ID[i] be the i-th index value of the index variable. +Then the output variable will be a tensor with shape [\(d_0\), +\(d_1\), ..., \(d_R\)]. If we treat the output tensor as a 2-D +matrix with shape [\(M\), \(N\)] and let \(O[i]\) be the i-th +row of the matrix, then O[i] is equal to \(I_{ID[i]}[i]\).

    -
    Parameters:
      -
    • x (Variable) – First operand of less_than
      • -
    • y (Variable) – Second operand of less_than
      • -
    • cond (Variable|None) – Optional output variable to store the result of less_than
      • +
    • inputs (list) – A list of variables to gather from. All variables have the +same shape and the rank is at least 2.
      • +
    • index (Variable) – Tensor<int32>, index variable which is a 2-D tensor +with shape [M, 1] where M is the batch size.
    Returns:

    The tensor variable storing the output of less_than.

    +
    Returns:

    Multiplex variable gathered from input variables.
    Return type:

    Variable

    @@ -2269,721 +3177,664 @@ LayerHelper.

    Examples

    -
    less = fluid.layers.less_than(x=label, y=limit)
+
    x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
+x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
+index = fluid.layers.data(name='index', shape=[1], dtype='int32')
+out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
    -
    -

    array_read

    +
    +
    +

    ops

    +
    +

    mean

    -paddle.v2.fluid.layers.array_read(array, i)
    -

    This function performs the operation to read the data in as an -LOD_TENSOR_ARRAY. -:param array: The input tensor that will be written to an array. -:type array: Variable|list -:param i: The subscript index in tensor array, that points the

    -
    -
    place where data will be written to.
    +paddle.v2.fluid.layers.mean(**kwargs) +

    Mean Operator.

    +

    Out is a scalar which is the mean of all elements in X.

    - + - +
    Returns:The tensor type variable that has the data written to it.
    Parameters:x – The input of mean op +Duplicable: False Optional: False
    Return type:Variable
    Returns:The output of mean op
    -

    Examples

    -
    - -
    -
    -

    shrink_memory

    -
    -
    -paddle.v2.fluid.layers.shrink_memory(x, i, table)
    -

    This function creates an operator to shrink_rnn_memory using the RankTable -as mentioned in the input parameter.

    -
    -

    array_length

    +
    +

    mul

    -paddle.v2.fluid.layers.array_length(array)
    -

    This function performs the operation to find the length of the input -LOD_TENSOR_ARRAY.

    +paddle.v2.fluid.layers.mul(**kwargs) +

    Mul Operator.

    +

    This operator is used to perform matrix multiplication for input $X$ and $Y$.

    +

    The equation is:

    +

    $$Out = X * Y$$

    +

    Both the input $X$ and $Y$ can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input $X$.

    - - - + - +
    Parameters:array (LOD_TENSOR_ARRAY) – The input array that will be used -to compute the length.
    Returns:The length of the input LoDTensorArray.
    Parameters:
      +
    • x – (Tensor), The first input tensor of mul op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of mul op. +Duplicable: False Optional: False
      • +
    • x_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two +dimensions as its inputs. If the input $X$ is a tensor with more +than two dimensions, $X$ will be flattened into a two-dimensional +matrix first. The flattening rule is: the first num_col_dims +will be flattened to form the first dimension of the final matrix +(the height of the matrix), and the rest rank(X) - num_col_dims +dimensions are flattened to form the second dimension of the final +matrix (the width of the matrix). As a result, height of the +flattened matrix is equal to the product of $X$’s first +x_num_col_dims dimensions’ sizes, and width of the flattened +matrix is equal to the product of $X$’s last rank(x) - num_col_dims +dimensions’ size. For example, suppose $X$ is a 6-dimensional +tensor with the shape [2, 3, 4, 5, 6], and x_num_col_dims = 3. +Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = +[24, 30].
      • +
    • y_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two, +dimensions as its inputs. If the input $Y$ is a tensor with more +than two dimensions, $Y$ will be flattened into a two-dimensional +matrix first. The attribute y_num_col_dims determines how $Y$ is +flattened. See comments of x_num_col_dims for more details.
      • +
    +
    Return type:Variable
    Returns:

    (Tensor), The output tensor of mul op.

    +
    -

    Examples

    -
    -

    conv2d_transpose

    +
    +

    reshape

    -paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
    -

    Convlution2D transpose layer

    -

    The convolution2D transpose layer calculates the output based on the input, -filter, and dilations, strides, paddings. Input(Input) and output(Output) -are in NCHW format. Where N is batch size, C is the number of channels, -H is the height of the feature, and W is the width of the feature. -Parameters(dilations, strides, paddings) are two elements. These two elements -represent height and width, respectively. The details of convolution transpose -layer, please refer to the following explanation and references -therein.

    -

    For each input \(X\), the equation is:

    -
    -\[Out = W \ast X\]
    -

    In the above equation:

    -
      -
    • \(X\): Input value, a tensor with NCHW format.
      • -
    • \(W\): Filter value, a tensor with MCHW format.
      • -
    • \(\ast\) : Convolution transpose operation.
      • -
    • -
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      -
      different.
      -
      -
      • -
    -

    Example

    -
      -

    • Input:

      -

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      -

      Filter shape: $(C_{in}, C_{out}, H_f, W_f)$

      -
      • -

    • Output:

      -

      Output shape: $(N, C_{out}, H_{out}, W_{out})$

      -
      • -
    -

    Where

    -
    -\[\begin{split}H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\ -W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]
    +paddle.v2.fluid.layers.reshape(**kwargs) +

    Reshape Operator.

    +

    Reshape Input(X) into the shape specified by Attr(shape).

    +

    An example: +Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]

    +

    and target shape = [1, 4], the reshape operator will transform +the tensor X into a 2-D tensor: [[1, 2, 3, 4]]

    +

    One dimension in the target shape can be set -1, representing that its +size is unknown. In this case, the real dimension will be infered from +the original shape of Input(X) and other dimensions in the target shape.

    - - - - -
    Parameters:
      -
    • input (Variable) – The input image with [N, C, H, W] format.
      • -
    • num_filters (int) – The number of the filter. It is as same as the output -image channel.
      • -
    • output_size (int|tuple|None) – The output image size. If output size is a -tuple, it must contain two integers, (image_H, image_W). This -parameter only works when filter_size is None.
      • -
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, -it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square. None if use output size to -calculate filter_size.
      • -
    • padding (int|tuple) – The padding size. If padding is a tuple, it must -contain two integers, (padding_H, padding_W). Otherwise, the -padding_H = padding_W = padding. Default: padding = 0.
      • -
    • stride (int|tuple) – The stride size. If stride is a tuple, it must -contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride. Default: stride = 1.
      • -
    • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must -contain two integers, (dilation_H, dilation_W). Otherwise, the -dilation_H = dilation_W = dilation. Default: dilation = 1.
      • -
    • param_attr (ParamAttr) – The parameters to the Conv2d_transpose Layer. -Default: None
      • -
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn -library is installed. Default: True
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – The input tensor of reshape operator. +Duplicable: False Optional: False
      • +
    • shape (INTS) – (vector<int>) Target shape of reshape operator.
    Returns:

    The tensor variable storing the convolution transpose result.

    -
    Return type:

    Variable

    -
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and -groups mismatch.

    +
    Returns:

    The output tensor of reshape operator.
    -

    Examples

    -
    data = fluid.layers.data(
-    name='data', shape=[3, 32, 32], dtype='float32')
-conv2d_transpose = fluid.layers.conv2d_transpose(
-    input=data, num_filters=2, filter_size=3)
-
    -
    -
    - -
    -
    -

    sequence_expand

    -
    -
    -paddle.v2.fluid.layers.sequence_expand(x, y, name=None)
    -

    Sequence Expand Layer. This layer will expand the input variable x -according to LoD information of y. And the following examples will -explain how sequence_expand works:

    -
    * Case 1
-    x is a LoDTensor:
-        x.lod = [[0,       2, 3],
-                 [0, 1,    3, 4]]
-        x.data = [a, b, c, d]
-        x.dims = [4, 1]
-
-    y is a LoDTensor:
-        y.lod = [[0,    2,    4],
-                 [0, 3, 6, 7, 8]]
-
-    with condition len(y.lod[-1]) - 1 == x.dims[0]
-
-    then output is a 2-level LoDTensor:
-        out.lod = [[0,                2,    4],
-                   [0,       3,       6, 7, 8]]
-        out.data = [a, a, a, b, b, b, c, d]
-        out.dims = [8, 1]
-
-* Case 2
-    x is a Tensor:
-        x.data = [a, b, c]
-        x.dims = [3, 1]
-
-    y is a LoDTensor:
-        y.lod = [[0, 2, 3, 6]]
-
-    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
    - then output is a 1-level LoDTensor: - out.lod = [[0, 2, 3, 6]] - out.data = [a, a, b, c, c, c] - out.dims = [6, 1] -
    +
    +

    scale

    +
    +
    +paddle.v2.fluid.layers.scale(**kwargs)
    +

    Scale operator

    +

    $$Out = scale*X$$

    - - -
    Parameters:
      -
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • y (Variable) – The input variable which is a LoDTensor.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor) Input tensor of scale operator. +Duplicable: False Optional: False
      • +
    • scale (FLOAT) – (float, default 1.0)The scaling factor of the scale operator.
    Returns:

    The expanded variable which is a LoDTensor.

    -
    Return type:

    Variable

    +
    Returns:

    (Tensor) Output tensor of scale operator.
    -

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10], dtype='float32')
-y = fluid.layers.data(name='y', shape=[10, 20],
-                 dtype='float32', lod_level=1)
-out = layers.sequence_expand(x=x, y=y)
-
    -
    -
    -

    gru_unit

    +
    +

    sigmoid_cross_entropy_with_logits

    -paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')
    -

    GRU unit layer. The equation of a gru step is:

    +paddle.v2.fluid.layers.sigmoid_cross_entropy_with_logits(**kwargs) +

    SigmoidCrossEntropyWithLogits Operator.

    +

    This measures the element-wise probability error in classification tasks +in which each class is independent. This can be thought of as predicting labels +for a data-point, where labels are not mutually exclusive. +For example, a news article can be about politics, technology or sports +at the same time or none of these.

    +

    The logistic loss is given as follows:

    -
    -\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)\\h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})\end{aligned}\end{align} \]
    -
    -

    The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms -of the equation above, the \(z_t\) is split into 3 parts - -\(xu_t\), \(xr_t\) and \(xm_t\). This means that in order to -implement a full GRU unit operator for an input, a fully -connected layer has to be applied, such that \(z_t = W_{fc}x_t\).

    -

    The terms \(u_t\) and \(r_t\) represent the update and reset gates -of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is -an intermediate candidate hidden output, which is denoted by \(m_t\). -This layer has three outputs \(h_t\), \(dot(r_t, h_{t-1})\) -and concatenation of \(u_t\), \(r_t\) and \(m_t\).

    +
    $$loss = -Labels * log(sigma(X)) - (1 - Labels) * log(1 - sigma(X))$$
    +

    We know that $$sigma(X) = (1 / (1 + exp(-X)))$$. By substituting this we get:

    +
    +
    $$loss = X - X * Labels + log(1 + exp(-X))$$
    +

    For stability and to prevent overflow of $$exp(-X)$$ when X < 0, +we reformulate the loss as follows:

    +
    +
    $$loss = max(X, 0) - X * Labels + log(1 + exp(-|X|))$$
    +

    Both the input X and Labels can carry the LoD (Level of Details) information. +However the output only shares the LoD with input X.

    - - -
    Parameters:
      -
    • input (Variable) – The fc transformed input value of current step.
      • -
    • hidden (Variable) – The hidden value of lstm unit from previous step.
      • -
    • size (integer) – The input dimension value.
      • -
    • weight (ParamAttr) – The weight parameters for gru unit. Default: None
      • -
    • bias (ParamAttr) – The bias parameters for gru unit. Default: None
      • -
    • activation (string) – The activation type for cell (actNode). -Default: ‘tanh’
      • -
    • gate_activation (string) – The activation type for gates (actGate). -Default: ‘sigmoid’
      • +
    • x – (Tensor, default Tensor<float>), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a tensor of logits computed by the previous operator. Logits are unscaled log probabilities given as log(p/(1-p)). +Duplicable: False Optional: False
      • +
    • label – (Tensor, default Tensor<float>), a 2-D tensor of the same type and shape as X. This input is a tensor of probabalistic labels for each logit +Duplicable: False Optional: False
    Returns:

    The hidden value, reset-hidden value and gate values.

    -
    Return type:

    tuple

    +
    Returns:

    (Tensor, default Tensor<float>), a 2-D tensor with shape N x D of elementwise logistic losses.
    -

    Examples

    -
    # assuming we have x_t_data and prev_hidden of size=10
-x_t = fluid.layers.fc(input=x_t_data, size=30)
-hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
-                                       hidden = prev_hidden)
+
    + +
    +
    +

    elementwise_add

    +
    +
    +paddle.v2.fluid.layers.elementwise_add(**kwargs)
    +

    Limited Elementwise Add Operator.

    +

    The equation is:

    +

    $$Out = X + Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    + +++ + + + + + +
    Parameters:
      +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    +
    Returns:

    The output of elementwise op.

    +
    -
    -

    lstm_unit

    +
    +

    elementwise_div

    -paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None, name=None)
    -

    Lstm unit layer. The equation of a lstm step is:

    -
    -
    -\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
    -
    -

    The inputs of lstm unit include \(x_t\), \(h_{t-1}\) and -\(c_{t-1}\). The 2nd dimensions of \(h_{t-1}\) and \(c_{t-1}\) -should be same. The implementation separates the linear transformation and -non-linear transformation apart. Here, we take \(i_t\) as an example. -The linear transformation is applied by calling a fc layer and the -equation is:

    -
    -
    -\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i\]
    -
    -

    The non-linear transformation is applied by calling lstm_unit_op and the -equation is:

    -
    -
    -\[i_t = \sigma(L_{i_t})\]
    -
    -

    This layer has two outputs including \(h_t\) and \(o_t\).

    +paddle.v2.fluid.layers.elementwise_div(**kwargs) +

    Limited Elementwise Div Operator.

    +

    The equation is:

    +

    $$Out = X / Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - +
    Parameters:
      -
    • x_t (Variable) – The input value of current step, a 2-D tensor with shape -M x N, M for batch size and N for input size.
      • -
    • hidden_t_prev (Variable) – The hidden value of lstm unit, a 2-D tensor -with shape M x S, M for batch size and S for size of lstm unit.
      • -
    • cell_t_prev (Variable) – The cell value of lstm unit, a 2-D tensor with -shape M x S, M for batch size and S for size of lstm unit.
      • -
    • forget_bias (float) – The forget bias of lstm unit.
      • -
    • param_attr (ParamAttr) – The attributes of parameter weights, used to set -initializer, name etc.
      • -
    • bias_attr (ParamAttr) – The attributes of bias weights, if not False, -bias weights will be created and be set to default value.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    Returns:

    The hidden value and cell value of lstm unit.

    +
    Returns:

    The output of elementwise op.
    Return type:

    tuple

    +
    +
    + +
    +
    +

    elementwise_sub

    +
    +
    +paddle.v2.fluid.layers.elementwise_sub(**kwargs)
    +

    Limited Elementwise Sub Operator.

    +

    The equation is:

    +

    $$Out = X - Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    + +++ + -
    Parameters:
      +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    Raises:

    ValueError – The ranks of x_t, hidden_t_prev and cell_t_prev -not be 2 or the 1st dimensions of x_t, hidden_t_prev -and cell_t_prev not be the same or the 2nd dimensions of -hidden_t_prev and cell_t_prev not be the same.

    +
    Returns:

    The output of elementwise op.
    -

    Examples

    -
    x_t = fluid.layers.fc(input=x_t_data, size=10)
-prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
-prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
-hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
-                                       hidden_t_prev=prev_hidden,
-                                       cell_t_prev=prev_cell)
+
    + +
    +
    +

    elementwise_mul

    +
    +
    +paddle.v2.fluid.layers.elementwise_mul(**kwargs)
    +

    Limited Elementwise Mul Operator.

    +

    The equation is:

    +

    $$Out = X odotY$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
    -
    - -
    -
    -

    sequence_softmax

    -
    -
    -paddle.v2.fluid.layers.sequence_softmax(**kwargs)
    -

    Sequence Softmax Operator.

    -

    SequenceSoftmaxOp computes the softmax activation among all time-steps for each -sequence. The dimension of each time-step should be 1. Thus, the shape of -input Tensor can be either [N, 1] or [N], where N is the sum of the length -of all sequences.

    -

    The algorithm works as follows:

    -
    -
    for i-th sequence in a mini-batch:
    -

    $$ -Out(X[lod[i]:lod[i+1]], :) = frac{exp(X[lod[i]:lod[i+1], :])} {sum(exp(X[lod[i]:lod[i+1], :]))} -$$

    -

    For example, for a mini-batch of 3 sequences with variable-length, -each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], -then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] -and N turns out to be 7.

    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - + - +
    Parameters:x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. -Duplicable: False Optional: False
    Parameters:
      +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    +
    Returns:(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.
    Returns:

    The output of elementwise op.

    +
    -
    -

    reduce_sum

    +
    +

    elementwise_max

    -paddle.v2.fluid.layers.reduce_sum(input, dim=None, keep_dim=False, name=None)
    -

    Computes the sum of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.elementwise_max(**kwargs) +

    Limited Elementwise Max Operator.

    +

    The equation is:

    +

    $$Out = max(X, Y)$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - -
    Parameters:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the sum is performed. If -None, sum all elements of input and return a -Tensor variable with a single element, otherwise must be in the -range \([-rank(input), rank(input))\). If \(dim < 0\), -the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    Returns:

    The reduced Tensor variable.

    -
    Return type:

    Variable

    +
    Returns:

    The output of elementwise op.
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_sum(x)  # [3.5]
-fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
-fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
-fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
-
    -
    -
    -

    reduce_mean

    +
    +

    elementwise_min

    -paddle.v2.fluid.layers.reduce_mean(input, dim=None, keep_dim=False, name=None)
    -

    Computes the mean of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.elementwise_min(**kwargs) +

    Limited Elementwise Max Operator.

    +

    The equation is:

    +

    $$Out = min(X, Y)$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - -
    Parameters:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the mean is computed. If -None, compute the mean over all elements of input -and return a Tensor variable with a single element, otherwise -must be in the range \([-rank(input), rank(input))\). If -\(dim < 0\), the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    Returns:

    The reduced Tensor variable.

    -
    Return type:

    Variable

    +
    Returns:

    The output of elementwise op.
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_mean(x)  # [0.4375]
-fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
-fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
-fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
-
    -
    -
    -

    reduce_max

    +
    +

    elementwise_pow

    -paddle.v2.fluid.layers.reduce_max(input, dim=None, keep_dim=False, name=None)
    -

    Computes the maximum of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.elementwise_pow(**kwargs) +

    Limited Elementwise Pow Operator.

    +

    The equation is:

    +

    $$Out = X ^ Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - -
    Parameters:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the maximum is computed. -If None, compute the maximum over all elements of -input and return a Tensor variable with a single element, -otherwise must be in the range \([-rank(input), rank(input))\). -If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    Returns:

    The reduced Tensor variable.

    -
    Return type:

    Variable

    +
    Returns:

    The output of elementwise op.
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_max(x)  # [0.9]
-fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
-fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
-fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
-
    -
    -
    -

    reduce_min

    +
    +

    clip

    -paddle.v2.fluid.layers.reduce_min(input, dim=None, keep_dim=False, name=None)
    -

    Computes the minimum of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.clip(**kwargs) +

    Clip Operator.

    +

    The clip operator limits the value of given input within an interval. The +interval is specified with arguments ‘min’ and ‘max’:

    +

    $$ +Out = min(max(X, min), max) +$$

    - - -
    Parameters:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the minimum is computed. -If None, compute the minimum over all elements of -input and return a Tensor variable with a single element, -otherwise must be in the range \([-rank(input), rank(input))\). -If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor)The input of clip op.The number of dimensions must be between [1, 9]. +Duplicable: False Optional: False
      • +
    • min (FLOAT) – (float)Minimum value, under which element is replaced by min.
      • +
    • max (FLOAT) – (float)Maximum value, above which element is replaced by max
    Returns:

    The reduced Tensor variable.

    -
    Return type:

    Variable

    +
    Returns:

    (Tensor)The output of clip op with shape as input(X)
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_min(x)  # [0.1]
-fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
-fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
-fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
-
    -
    -
    -

    split

    +
    +

    clip_by_norm

    -paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None)
    -

    Split the input tensor into multiple sub-tensors.

    +paddle.v2.fluid.layers.clip_by_norm(**kwargs) +

    ClipByNorm Operator.

    +

    This operator limits the L2 norm of the input $X$ within $max_norm$. +If the L2 norm of $X$ is less than or equal to $max_norm$, $Out$ will be +the same as $X$. If the L2 norm of $X$ is greater than $max_norm$, $X$ will +be linearly scaled to make the L2 norm of $Out$ equal to $max_norm$, as +shown in the following formula:

    +

    $$ +Out = frac{max_norm * X}{norm(X)}, +$$

    +

    where $norm(X)$ represents the L2 norm of $X$.

    - - -
    Parameters:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • num_or_sections (int|list) – If num_or_sections is an integer, -then the integer indicates the number of equal sized sub-tensors -that the tensor will be divided into. If num_or_sections -is a list of integers, the length of list indicates the number of -sub-tensors and the integers indicate the sizes of sub-tensors’ -dim dimension orderly.
      • -
    • dim (int) – The dimension along which to split. If \(dim < 0\), the -dimension to split along is \(rank(input) + dim\).
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor) The input of clip_by_norm op.The number of dimensions must be between [1, 9]. +Duplicable: False Optional: False
      • +
    • max_norm (FLOAT) – (float) The maximum norm value.
    Returns:

    The list of segmented tensor variables.

    -
    Return type:

    List

    +
    Returns:

    (Tensor) The output of clip_by_norm op with shape as input(X)
    -

    Examples

    -
    # x is a Tensor variable with shape [3, 9, 5]:
-x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
-x0.shape  # [3, 3, 5]
-x1.shape  # [3, 3, 5]
-x2.shape  # [3, 3, 5]
-x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
-x0.shape  # [3, 2, 5]
-x1.shape  # [3, 3, 5]
-x2.shape  # [3, 4, 5]
-
    -
    -
    -

    matmul

    +
    +

    sequence_softmax

    -paddle.v2.fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, name=None)
    -

    Applies matrix multiplication to two tensors.

    -

    Currently, the input tensors’ rank can be any, but when the rank of any -inputs is bigger than 3, this two inputs’ rank should be equal.

    -

    The actual behavior depends on the shapes of \(x\), \(y\) and the -flag values of transpose_x, transpose_y. Specifically:

    -
      -
    • If a transpose flag is specified, the last two dimensions of the tensor -are transposed. If the tensor is rank-1 of shape \([D]\), then for -\(x\) it is treated as \([1, D]\) in nontransposed form and as -\([D, 1]\) in transposed form, whereas for \(y\) it is the -opposite: It is treated as \([D, 1]\) in nontransposed form and as -\([1, D]\) in transposed form.
      • -
    • After transpose, the two tensors are 2-D or n-D and matrix multiplication -performs in the following way.
        -
      • If both are 2-D, they are multiplied like conventional matrices.
        • -
      • If either is n-D, it is treated as a stack of matrices residing in the -last two dimensions and a batched matrix multiply supporting broadcast -applies on the two tensors.
        • -
      -
      • -
    -

    Also note that if the raw tensor \(x\) or \(y\) is rank-1 and -nontransposed, the prepended or appended dimension \(1\) will be -removed after matrix multiplication.

    +paddle.v2.fluid.layers.sequence_softmax(**kwargs) +

    Sequence Softmax Operator.

    +

    SequenceSoftmaxOp computes the softmax activation among all time-steps for each +sequence. The dimension of each time-step should be 1. Thus, the shape of +input Tensor can be either [N, 1] or [N], where N is the sum of the length +of all sequences.

    +

    The algorithm works as follows:

    +
    +
    for i-th sequence in a mini-batch:
    +

    $$ +Out(X[lod[i]:lod[i+1]], :) = frac{exp(X[lod[i]:lod[i+1], :])} {sum(exp(X[lod[i]:lod[i+1], :]))} +$$

    +

    For example, for a mini-batch of 3 sequences with variable-length, +each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], +then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] +and N turns out to be 7.

    - - - + - +
    Parameters:
      -
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • y (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • transpose_x (bool) – Whether to transpose \(x\) before multiplication.
      • -
    • transpose_y (bool) – Whether to transpose \(y\) before multiplication.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • -
    -
    Returns:

    The product Tensor variable.

    -
    Parameters:x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. +Duplicable: False Optional: False
    Return type:

    Variable

    -
    Returns:(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.
    -

    Examples

    -
    # Examples to clarify shapes of the inputs and output
-# x: [B, ..., M, K], y: [B, ..., K, N]
-fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
-
-# x: [B, M, K], y: [B, K, N]
-fluid.layers.matmul(x, y)  # out: [B, M, N]
-
-# x: [B, M, K], y: [K, N]
-fluid.layers.matmul(x, y)  # out: [B, M, N]
-
-# x: [M, K], y: [K, N]
-fluid.layers.matmul(x, y)  # out: [M, N]
-
-# x: [B, M, K], y: [K]
-fluid.layers.matmul(x, y)  # out: [B, M]
-
-# x: [K], y: [K]
-fluid.layers.matmul(x, y)  # out: [1]
+
    -# x: [M], y: [N] -fluid.layers.matmul(x, y, True, True) # out: [M, N] -
    +
    +

    sigmoid

    +
    +
    +paddle.v2.fluid.layers.sigmoid(**kwargs)
    +

    Sigmoid Activation Operator

    +

    $$out = frac{1}{1 + e^{-x}}$$

    + +++ + + + + + +
    Parameters:x – Input of Sigmoid operator +Duplicable: False Optional: False
    Returns:Output of Sigmoid operator
    -

    logsigmoid

    +

    logsigmoid

    paddle.v2.fluid.layers.logsigmoid(**kwargs)
    @@ -3004,7 +3855,7 @@ Duplicable: False Optional: False
    -

    exp

    +

    exp

    paddle.v2.fluid.layers.exp(**kwargs)
    @@ -3025,7 +3876,7 @@ Duplicable: False Optional: False
    -

    relu

    +

    relu

    paddle.v2.fluid.layers.relu(**kwargs)
    @@ -3046,7 +3897,7 @@ Duplicable: False Optional: False
    -

    tanh

    +

    tanh

    paddle.v2.fluid.layers.tanh(**kwargs)
    @@ -3067,7 +3918,7 @@ Duplicable: False Optional: False
    -

    tanh_shrink

    +

    tanh_shrink

    paddle.v2.fluid.layers.tanh_shrink(**kwargs)
    @@ -3088,7 +3939,7 @@ Duplicable: False Optional: False
    -

    softshrink

    +

    softshrink

    paddle.v2.fluid.layers.softshrink(**kwargs)
    @@ -3121,7 +3972,7 @@ Duplicable: False Optional: False
    -

    sqrt

    +

    sqrt

    paddle.v2.fluid.layers.sqrt(**kwargs)
    @@ -3142,7 +3993,7 @@ Duplicable: False Optional: False
    -

    abs

    +

    abs

    paddle.v2.fluid.layers.abs(**kwargs)
    @@ -3163,7 +4014,7 @@ Duplicable: False Optional: False
    -

    ceil

    +

    ceil

    paddle.v2.fluid.layers.ceil(**kwargs)
    @@ -3184,7 +4035,7 @@ Duplicable: False Optional: False
    -

    floor

    +

    floor

    paddle.v2.fluid.layers.floor(**kwargs)
    @@ -3205,7 +4056,7 @@ Duplicable: False Optional: False
    -

    round

    +

    round

    paddle.v2.fluid.layers.round(**kwargs)
    @@ -3226,7 +4077,7 @@ Duplicable: False Optional: False
    -

    reciprocal

    +

    reciprocal

    paddle.v2.fluid.layers.reciprocal(**kwargs)
    @@ -3247,7 +4098,7 @@ Duplicable: False Optional: False
    -

    log

    +

    log

    paddle.v2.fluid.layers.log(**kwargs)
    @@ -3269,7 +4120,7 @@ Duplicable: False Optional: False
    -

    square

    +

    square

    paddle.v2.fluid.layers.square(**kwargs)
    @@ -3290,7 +4141,7 @@ Duplicable: False Optional: False
    -

    softplus

    +

    softplus

    paddle.v2.fluid.layers.softplus(**kwargs)
    @@ -3311,12 +4162,12 @@ Duplicable: False Optional: False
    -

    softsign

    +

    softsign

    paddle.v2.fluid.layers.softsign(**kwargs)

    Softsign Activation Operator.

    -

    $$out = frac{x}{1 + |x|}$$

    +

    $$out = frac{x}{1 + |x|}$$

    @@ -3332,7 +4183,7 @@ Duplicable: False Optional: False
    -

    brelu

    +

    brelu

    paddle.v2.fluid.layers.brelu(**kwargs)
    @@ -3359,7 +4210,7 @@ Duplicable: False Optional: False
    -

    leaky_relu

    +

    leaky_relu

    paddle.v2.fluid.layers.leaky_relu(**kwargs)
    @@ -3385,7 +4236,7 @@ Duplicable: False Optional: False
    -

    soft_relu

    +

    soft_relu

    paddle.v2.fluid.layers.soft_relu(**kwargs)
    @@ -3411,7 +4262,7 @@ Duplicable: False Optional: False
    -

    elu

    +

    elu

    paddle.v2.fluid.layers.elu(**kwargs)
    @@ -3439,7 +4290,7 @@ Duplicable: False Optional: False
    -

    relu6

    +

    relu6

    paddle.v2.fluid.layers.relu6(**kwargs)
    @@ -3465,7 +4316,7 @@ Duplicable: False Optional: False
    -

    pow

    +

    pow

    paddle.v2.fluid.layers.pow(**kwargs)
    @@ -3489,9 +4340,36 @@ Duplicable: False Optional: False
    +
    +
    +

    stanh

    +
    +
    +paddle.v2.fluid.layers.stanh(**kwargs)
    +

    STanh Activation Operator.

    +

    $$out = b * frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$

    + +++ + + + + + +
    Parameters:
      +
    • x – Input of STanh operator +Duplicable: False Optional: False
      • +
    • scale_a (FLOAT) – The scale parameter of a for the input
      • +
    • scale_b (FLOAT) – The scale parameter of b for the input
      • +
    +
    Returns:

    Output of STanh operator

    +
    +
    +
    -

    hard_shrink

    +

    hard_shrink

    paddle.v2.fluid.layers.hard_shrink(**kwargs)
    @@ -3524,7 +4402,7 @@ Duplicable: False Optional: False
    -

    thresholded_relu

    +

    thresholded_relu

    paddle.v2.fluid.layers.thresholded_relu(**kwargs)
    @@ -3556,7 +4434,7 @@ Duplicable: False Optional: False
    -

    hard_sigmoid

    +

    hard_sigmoid

    paddle.v2.fluid.layers.hard_sigmoid(**kwargs)
    @@ -3588,7 +4466,7 @@ Duplicable: False Optional: False
    -

    swish

    +

    swish

    paddle.v2.fluid.layers.swish(**kwargs)
    @@ -3613,169 +4491,143 @@ Duplicable: False Optional: False
    -
    -

    im2sequence

    +
    +
    +

    tensor

    +
    +

    create_tensor

    -paddle.v2.fluid.layers.im2sequence(input, filter_size=1, stride=1, padding=0, name=None)
    -

    Extracts image patches from the input tensor to form a tensor of shape -{input.batch_size * output_height * output_width, filter_size_H * -filter_size_W * input.channels} which is similar with im2col. -This op use filter / kernel to scan images and convert these images to -sequences. After expanding, the number of time step are -output_height * output_width for an image, in which output_height and -output_width are calculated by below equation:

    -
    -\[output\_size = 1 + (2 * padding + img\_size - block\_size + stride - 1) / stride\]
    -

    And the dimension of each time step is block_y * block_x * input.channels.

    +paddle.v2.fluid.layers.create_tensor(dtype, name=None) +
    + +
    +
    +

    create_parameter

    +
    +
    +paddle.v2.fluid.layers.create_parameter(shape, dtype, attr=None, is_bias=False, default_initializer=None)
    +

    Create a parameter +:param shape: shape of the parameter +:type shape: list[int] +:param dtype: element type of the parameter +:type dtype: string +:param attr: attributes of the parameter +:type attr: ParamAttr +:param is_bias: This can affect which default initializer is chosen

    +
    +
    when default_initializer is None. If is_bias, +initializer.Constant(0.0) will be used. Otherwise, +Xavier() will be used.
    - + - + - +
    Parameters:
      -
    • input (Variable) – The input should be a tensor in NCHW format.
      • -
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, -it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square.
      • -
    • stride (int|tuple) – The stride size. If stride is a tuple, it must -contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride. Default: stride = 1.
      • -
    • padding (int|tuple) – The padding size. If padding is a tuple, it can -contain two integers like (padding_H, padding_W) which means -padding_up = padding_down = padding_H and -padding_left = padding_right = padding_W. Or it can use -(padding_up, padding_left, padding_down, padding_right) to indicate -paddings of four direction. Otherwise, a scalar padding means -padding_up = padding_down = padding_left = padding_right = padding -Default: padding = 0.
      • -
    • name (int) – The name of this layer. It is optional.
      • -
    -
    Parameters:default_initializer (Initializer) – initializer for the parameter
    Returns:

    The output is a LoDTensor with shape -{input.batch_size * output_height * output_width, -filter_size_H * filter_size_W * input.channels}. -If we regard output as a matrix, each row of this matrix is -a step of a sequence.

    -
    Returns:the created parameter
    Return type:

    output

    -
    Return type:Parameter
    -

    Examples:

    -

    As an example:

    -
    -
    Given:
-
-x = [[[[ 6.  2.  1.]
-       [ 8.  3.  5.]
-       [ 0.  2.  6.]]
-
-      [[ 2.  4.  4.]
-       [ 6.  3.  0.]
-       [ 6.  4.  7.]]]
-
-     [[[ 6.  7.  1.]
-       [ 5.  7.  9.]
-       [ 2.  4.  8.]]
-
-      [[ 1.  2.  1.]
-       [ 1.  3.  5.]
-       [ 9.  0.  8.]]]]
-
-x.dims = {2, 2, 3, 3}
-
-And:
-
-filter = [2, 2]
-stride = [1, 1]
-padding = [0, 0]
-
-Then:
-
-output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
-               [ 2.  1.  3.  5.  4.  4.  3.  0.]
-               [ 8.  3.  0.  2.  6.  3.  6.  4.]
-               [ 3.  5.  2.  6.  3.  0.  4.  7.]
-               [ 6.  7.  5.  7.  1.  2.  1.  3.]
-               [ 7.  1.  7.  9.  2.  1.  3.  5.]
-               [ 5.  7.  2.  4.  1.  3.  9.  0.]
-               [ 7.  9.  4.  8.  3.  5.  0.  8.]]
+
    -output.dims = {8, 9} +
    +
    +

    create_global_var

    +
    +
    +paddle.v2.fluid.layers.create_global_var(shape, value, dtype, persistable=False, name=None)
    +
    -output.lod = [[0, 4, 8]] -
    -

    The simple usage is:

    -
    output = fluid.layers.im2sequence(
-    input=layer, stride=[1, 1], filter_size=[2, 2])
-
    +
    +

    cast

    +
    +
    +paddle.v2.fluid.layers.cast(x, dtype)
    +

    This function takes in the input with input_dtype +and casts it to the output_dtype as the output.

    +
    +
    -
    +
    +

    concat

    +
    +
    +paddle.v2.fluid.layers.concat(input, axis=0)
    +

    Concat

    +

    This function concatenates the input along the axis mentioned +and returns that as the output.

    + +++ + + + + + + + +
    Parameters:
      +
    • input (list) – List of tensors to be concatenated
      • +
    • axis (int) – Integer axis along which the tensors will be concatenated
      • +
    +
    Returns:

    Output variable of the concatenation

    +
    Return type:

    Variable

    +
    +

    Examples

    -
    -

    edit_distance

    -
    -
    -

    ctc_greedy_decoder

    +
    +

    sums

    -paddle.v2.fluid.layers.ctc_greedy_decoder(input, blank, name=None)
    -

    This op is used to decode sequences by greedy policy by below steps: -1. Get the indexes of max value for each row in input. a.k.a.

    -
    -
    numpy.argmax(input, axis=0).
    -
      -
    1. For each sequence in result of step1, merge repeated tokens between two -blanks and delete all blanks.
      2. -
    -

    A simple example as below:

    -
    Given:
-
-input.data = [[0.6, 0.1, 0.3, 0.1],
-              [0.3, 0.2, 0.4, 0.1],
-              [0.1, 0.5, 0.1, 0.3],
-              [0.5, 0.1, 0.3, 0.1],
-
-              [0.5, 0.1, 0.3, 0.1],
-              [0.2, 0.2, 0.2, 0.4],
-              [0.2, 0.2, 0.1, 0.5],
-              [0.5, 0.1, 0.3, 0.1]]
-
-input.lod = [[0, 4, 8]]
-
-Then:
-
-output.data = [[2],
-               [1],
-               [3]]
+paddle.v2.fluid.layers.sums(input, out=None)
+
    This function performs the sum operation on the input and returns the
+result as the output.
    
+
+++
+
+
+
+
+
+
+
+
    


    Parameters:input (Variable|list) – The input tensor that has the elements
+that need to be summed up.
    Returns:
    
+
    The tensor type variable that has the sum of input
    
+
    written to it.
    
+
    
+
    Return type:Variable
    
+
    Examples
    
+
    -output.lod = [[0, 2, 3]] -
    +
    +

    assign

    +
    +
    +paddle.v2.fluid.layers.assign(input, output)
    +

    Assign

    +

    This function copies the input Variable to the output Variable.

    -
    Parameters:
      -
    • input (Variable) – (LoDTensor<float>), the probabilities of -variable-length sequences, which is a 2-D Tensor with -LoD information. It’s shape is [Lp, num_classes + 1], -where Lp is the sum of all input sequences’ length and -num_classes is the true number of classes. (not -including the blank label).
      • -
    • blank (int) – the blank label index of Connectionist Temporal -Classification (CTC) loss, which is in thehalf-opened -interval [0, num_classes + 1).
      • +
    • input (Variable|numpy.ndarray) – The source variable
      • +
    • output (Variable) – The destination variable
    Returns:

    CTC greedy decode result.

    +
    Returns:

    The destination variable that was supplied as the output.
    Return type:

    Variable

    @@ -3784,41 +4636,34 @@ interval [0, num_classes + 1).

    Examples

    -
    x = fluid.layers.data(name='x', shape=[8], dtype='float32')
-
-cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
-
    -
    -
    -

    l2_normalize

    +
    +

    fill_constant_batch_size_like

    -paddle.v2.fluid.layers.l2_normalize(x, axis, epsilon=1e-12, name=None)
    -

    L2 normalize Layer

    -

    The l2 normalize layer normalizes x along dimension axis using an L2 -norm. For a 1-D tensor (dim is fixed to 0), this layer computes

    -

    output = x / sqrt(max(sum(x**2), epsilon))

    -

    For x with more dimensions, this layer independently normalizes each 1-D -slice along dimension axis.

    +paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0) +

    fill_constant_batch_size_like

    +

    This function creates a tensor of specified shape, dtype and batch size, +and initializes this with a constant supplied in value. The batch size is +obtained from the input tensor.

    +

    It also sets stop_gradient to True.

    -
    Parameters:
      -
    • x (Variable|list) – The input tensor to l2_normalize layer.
      • -
    • axis (int) – Dimension along which to normalize the input.
      • -
    • epsilon (float) – A lower bound value for x‘s l2 norm. sqrt(epsilon) will -be used as the divisor if the l2 norm of x is less than -sqrt(epsilon).
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • input (Variable) – Tensor whose dimensions will be used to get batch size
      • +
    • shape (tuple|list|None) – Shape of output tensor
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • +
    • value (float) – Constant value to initialize the output tensor
      • +
    • input_dim_idx (int) – Index of input’s batch size dimension
      • +
    • output_dim_idx (int) – Index of output’s batch size dimension
    Returns:

    The output tensor variable.

    +
    Returns:

    The tensor variable storing the output
    Return type:

    Variable

    @@ -3827,55 +4672,35 @@ will be named automatically.

    Examples

    -
    data = fluid.layers.data(name="data",
-                         shape=(3, 17, 13),
-                         dtype="float32")
-normed = fluid.layers.l2_normalize(x=data, axis=1)
+
    data = fluid.layers.fill_constant_batch_size_like(
+    input=like, shape=[1], value=0, dtype='int64')
    -
    -

    sequence_reshape

    +
    +

    fill_constant

    -paddle.v2.fluid.layers.sequence_reshape(input, new_dim)
    -

    Sequence Reshape Layer

    -

    This layer will rearrange the input sequences. The new dimension is set by -user. Length of each sequence is computed according to original length, -original dimension and new dimension. The following example will help to -illustrate the function of this layer:

    -
    x is a LoDTensor:
-    x.lod  = [[0, 2, 6]]
-    x.data = [[1, 2], [3, 4],
-              [5, 6], [7, 8], [9, 10], [11, 12]]
-    x.dims = [6, 2]
-
-set new_dim = 4
-
-then out is a LoDTensor:
-    out.lod  = [[0, 1, 3]]
-    out.data = [[1, 2, 3, 4],
-                [5, 6, 7, 8], [9, 10, 11, 12]]
-    out.dims = [3, 4]
-
    -
    -

    Currently, only 1-level LoDTensor is supported and please make sure -(original length * original dimension) can be divided by new dimension with -no remainder for each sequence.

    +paddle.v2.fluid.layers.fill_constant(shape, dtype, value, force_cpu=False, out=None) +

    fill_constant

    +

    This function creates a tensor with specified shape and dtype, and +initializes it with a constant specifed by value.

    +

    The attribute stop_gradient of the created tensor is set to True.

    -
    Parameters:
      -
    • input (Variable) – (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor -with shape being [N, M] where M for dimension.
      • -
    • new_dim (int) – New dimension which the input LoDTensor is reshaped to.
      • +
    • shape (tuple|list|None) – Shape of the output tensor.
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of the output tensor.
      • +
    • value (float) – The constant value used to initialize the output tensor.
      • +
    • out (Variable) – The output tensor.
    Returns:

    Reshaped LoDTensor according to new dimension.

    +
    Returns:

    The tensor variable storing the output.
    Return type:

    Variable

    @@ -3884,49 +4709,32 @@ with shape being [N, M] where M for dimension.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[5, 20],
-                  dtype='float32', lod_level=1)
-x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
+
    data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
    -
    -

    row_conv

    +
    +

    ones

    -paddle.v2.fluid.layers.row_conv(input, future_context_size, param_attr=None, act=None)
    -

    Row Conv Operator. This layer will apply lookahead convolution to -input. The input variable should be a 2D LoDTensor with shape [T, D]. -Parameters with shape [future_context_size + 1, D] will be created. The math -equation of row convolution is as follows:

    -
    -\[Out_{i} = \sum_{j = i} ^ {i + \tau} X_{j} \odot W_{i - j}\]
    -

    In the above equation:

    -
      -
    • \(Out_{i}\): The i-th row of output variable with shape [1, D].
      • -
    • \(\tau\): Future context size.
      • -
    • \(X_{j}\): The j-th row of input variable with shape [1, D].
      • -
    • \(W_{i-j}\): The (i-j)-th row of parameters with shape [1, D].
      • -
    -

    More details about row_conv please refer to the paper (http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and -the design document (https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).

    +paddle.v2.fluid.layers.ones(shape, dtype) +

    ones

    +

    This function creates a tensor of specified shape and +dtype, and initializes this with 1.

    +

    It also sets stop_gradient to True.

    -
    Parameters:
      -
    • input (Variable) – Input variable, a 2D LoDTensor with shape [T, D].
      • -
    • future_context_size (int) – Future context size. Please note, the shape -of convolution kernel is [future_context_size + 1, D].
      • -
    • param_attr (ParamAttr) – Attributes of parameters, including -name, initializer etc.
      • -
    • act (str) – Non-linear activation to be applied to output variable.
      • +
    • shape (tuple|list|None) – Shape of output tensor
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    Returns:

    The output tensor with same shape as input tensor.

    +
    Returns:

    The tensor variable storing the output
    Return type:

    Variable

    @@ -3935,48 +4743,32 @@ name, initializer etc.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[16],
-                dtype='float32', lod_level=1)
-out = fluid.layers.row_conv(input=x, future_context_size=2)
+
    data = fluid.layers.ones(shape=[1], dtype='int64')
    -
    -

    multiplex

    +
    +

    zeros

    -paddle.v2.fluid.layers.multiplex(inputs, index)
    -

    Multiplex Layer

    -

    Referring to the given index variable, this layer selects rows from the -input variables to construct a multiplex variable. Assuming that there are -\(m\) input variables and \(I_i\) represents the i-th input -variable and \(i\) is in [0, \(m\)). All input variables are -tensors with same shape [\(d_0\), \(d_1\), ..., \(d_R\)]. -Please note that rank of the input tensor should be at least 2. Each input -variable will be treated as a 2-D matrix with shape [\(M\), \(N\)] -where \(M\) for \(d_0\) and \(N\) for \(d_1\) * \(d_2\) -* ... * \(d_R\). Let \(I_i[j]\) be the j-th row of the i-th input -variable. The given index variable should be a 2-D tensor with shape -[\(M\), 1]. Let ID[i] be the i-th index value of the index variable. -Then the output variable will be a tensor with shape [\(d_0\), -\(d_1\), ..., \(d_R\)]. If we treat the output tensor as a 2-D -matrix with shape [\(M\), \(N\)] and let \(O[i]\) be the i-th -row of the matrix, then O[i] is equal to \(I_{ID[i]}[i]\).

    +paddle.v2.fluid.layers.zeros(shape, dtype) +

    zeros

    +

    This function creates a tensor of specified shape and +dtype, and initializes this with 0.

    +

    It also sets stop_gradient to True.

    -
    Parameters:
      -
    • inputs (list) – A list of variables to gather from. All variables have the -same shape and the rank is at least 2.
      • -
    • index (Variable) – Tensor<int32>, index variable which is a 2-D tensor -with shape [M, 1] where M is the batch size.
      • +
    • shape (tuple|list|None) – Shape of output tensor
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    Returns:

    Multiplex variable gathered from input variables.

    +
    Returns:

    The tensor variable storing the output
    Return type:

    Variable

    @@ -3985,14 +4777,12 @@ with shape [M, 1] where M is the batch size.

    Examples

    -
    x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
-x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
-index = fluid.layers.data(name='index', shape=[1], dtype='int32')
-out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
+
    data = fluid.layers.zeros(shape=[1], dtype='int64')
    +
    @@ -4003,7 +4793,7 @@ with shape [M, 1] where M is the batch size. @@ -217,7 +217,7 @@
    -

    Nets

    +

    nets

    simple_img_conv_pool

    @@ -225,15 +225,6 @@ paddle.v2.fluid.nets.simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, param_attr=None, pool_type='max', use_cudnn=True)
    -
    -
    -

    img_conv_group

    -
    -
    -paddle.v2.fluid.nets.img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, param_attr=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=0.0, pool_stride=1, pool_type=None, use_cudnn=True)
    -

    Image Convolution Group, Used for vgg net.

    -
    -

    sequence_conv_pool

    @@ -361,10 +352,10 @@ parameters.

    diff --git a/develop/doc/api/v2/fluid/optimizer.html b/develop/doc/api/v2/fluid/optimizer.html index bd08781ae0..e75efcc1cb 100644 --- a/develop/doc/api/v2/fluid/optimizer.html +++ b/develop/doc/api/v2/fluid/optimizer.html @@ -8,7 +8,7 @@ - Optimizer — PaddlePaddle documentation + optimizer — PaddlePaddle documentation @@ -34,8 +34,8 @@ - - + + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • Optimizer
    • +
  • optimizer
    • @@ -217,113 +217,58 @@
    -

    Optimizer

    -
    -

    Optimizer

    -
    -
    -class paddle.v2.fluid.optimizer.Optimizer(learning_rate, global_step=None, regularization=None)
    -

    Optimizer Base class.

    -

    Define the common interface of an optimizer. -User should not use this class directly, -but need to use one of it’s implementation.

    +

    optimizer

    +
    +

    SGD

    -global_learning_rate
    -

    get global decayed learning rate -:return:

    -
    - -
    -
    -create_optimization_pass(parameters_and_grads, loss, startup_program=None)
    -

    Add optimization operators to update gradients to variables.

    - --- - - - - - - - -
    Parameters:
      -
    • loss – the target that this optimization is for.
      • -
    • parameters_and_grads – a list of (variable, gradient) pair to update.
      • -
    -
    Returns:

    a list of operators that will complete one step of -optimization. This will include parameter update ops, global step -update ops and any other custom ops required by subclasses to manage -their internal state. -:param startup_program:

    -
    Return type:

    return_op_list

    -
    -
    - -
    -
    -minimize(loss, startup_program=None, parameter_list=None, no_grad_set=None)
    -

    Add operations to minimize loss by updating parameter_list.

    -

    This method combines interface append_backward() and -create_optimization_pass() into one.

    -
    - +paddle.v2.fluid.optimizer.SGD +

    alias of SGDOptimizer

    -
    -

    SGDOptimizer

    -
    -
    -class paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, **kwargs)
    -

    Simple SGD optimizer without any state.

    -
    - -
    -
    -

    MomentumOptimizer

    -
    +
    +

    Momentum

    +
    -class paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, **kwargs)
    -

    Simple Momentum optimizer with velocity state

    +paddle.v2.fluid.optimizer.Momentum +

    alias of MomentumOptimizer

    -
    -

    AdagradOptimizer

    -
    +
    +

    Adagrad

    +
    -class paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, **kwargs)
    -

    Simple Adagrad optimizer with moment state

    +paddle.v2.fluid.optimizer.Adagrad +

    alias of AdagradOptimizer

    -
    -

    AdamOptimizer

    -
    +
    +

    Adam

    +
    -class paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
    -

    Implements the Adam Optimizer

    +paddle.v2.fluid.optimizer.Adam +

    alias of AdamOptimizer

    -
    -

    AdamaxOptimizer

    -
    +
    +

    Adamax

    +
    -class paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
    -

    Implements the Adamax Optimizer

    +paddle.v2.fluid.optimizer.Adamax +

    alias of AdamaxOptimizer

    -
    -

    DecayedAdagradOptimizer

    -
    +
    +

    DecayedAdagrad

    +
    -class paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, **kwargs)
    -

    Simple Decayed Adagrad optimizer with moment state

    +paddle.v2.fluid.optimizer.DecayedAdagrad +

    alias of DecayedAdagradOptimizer

    @@ -336,10 +281,10 @@ their internal state. diff --git a/develop/doc/api/v2/fluid/param_attr.html b/develop/doc/api/v2/fluid/param_attr.html index 6ba47f50fb..d3e178399d 100644 --- a/develop/doc/api/v2/fluid/param_attr.html +++ b/develop/doc/api/v2/fluid/param_attr.html @@ -8,7 +8,7 @@ - ParamAttr — PaddlePaddle documentation + param_attr — PaddlePaddle documentation @@ -34,8 +34,8 @@ - - + + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • ParamAttr
    • +
  • param_attr
    • @@ -216,10 +216,26 @@
    -
    -

    ParamAttr

    -
    -

    ParamAttr

    +
    +

    param_attr

    +
    +

    ParamAttr

    +
    +
    +class paddle.v2.fluid.param_attr.ParamAttr(name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, gradient_clip=None)
    +
    + +
    +
    +

    WeightNormParamAttr

    +
    +
    +class paddle.v2.fluid.param_attr.WeightNormParamAttr(dim=None, **kwargs)
    +

    Used for weight normalization. Any field in ParamAttr can also be set here. +Besides, an extra field dim can be set to indicate the dimension except +which to normalize.

    +
    +
    @@ -230,10 +246,10 @@ diff --git a/develop/doc/api/v2/fluid/profiler.html b/develop/doc/api/v2/fluid/profiler.html index cda12d3fe0..e2e4c0d110 100644 --- a/develop/doc/api/v2/fluid/profiler.html +++ b/develop/doc/api/v2/fluid/profiler.html @@ -8,7 +8,7 @@ - Profiler — PaddlePaddle documentation + profiler — PaddlePaddle documentation @@ -34,8 +34,8 @@ - - + + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • Profiler
    • +
  • profiler
    • @@ -217,9 +217,9 @@
    -

    Profiler

    -
    -

    Profiler

    +

    profiler

    +
    +

    cuda_profiler

    paddle.v2.fluid.profiler.cuda_profiler(*args, **kwds)
    @@ -249,6 +249,53 @@ to “Compute Command Line Profiler User Guide”.
    +
    +
    +

    reset_profiler

    +
    +
    +paddle.v2.fluid.profiler.reset_profiler()
    +

    The profiler clear interface. +reset_profiler will clear the previous time record.

    +
    + +
    +
    +

    profiler

    +
    +
    +paddle.v2.fluid.profiler.profiler(*args, **kwds)
    +

    The profiler interface. +Different from cuda_profiler, this profiler can be used to profile both CPU +and GPU program. By defalut, it records the CPU and GPU operator kernels, +if you want to profile other program, you can refer the profiling tutorial +to add more records.

    + +++ + + + +
    Parameters:
      +
    • state (string) – The profiling state, which should be ‘CPU’ or ‘GPU’, +telling the profiler to use CPU timer or GPU timer for profiling. +Although users may have already specified the execution place +(CPUPlace/CUDAPlace) in the begining, for flexibility the profiler +would not inherit this place.
      • +
    • sorted_key (string) – If None, the profiling results will be printed +in the order of first end time of events. Otherwise, the profiling +results will be sorted by the this flag. This flag should be one +of ‘calls’, ‘total’, ‘max’, ‘min’ or ‘ave’. +The calls means sorting by the number of calls. +The total means sorting by the total execution time. +The max means sorting by the maximum execution time. +The min means sorting by the minimum execution time. +The ave means sorting by the average execution time.
      • +
    +
    +
    +
    @@ -259,10 +306,10 @@ to “Compute Command Line Profiler User Guide”. diff --git a/develop/doc/api/v2/fluid/regularizer.html b/develop/doc/api/v2/fluid/regularizer.html index b1c507121b..6ab82c163f 100644 --- a/develop/doc/api/v2/fluid/regularizer.html +++ b/develop/doc/api/v2/fluid/regularizer.html @@ -8,7 +8,7 @@ - Regularizer — PaddlePaddle documentation + regularizer — PaddlePaddle documentation @@ -34,8 +34,8 @@ - - + + @@ -162,17 +162,17 @@
  • Training and Inference
  • Fluid
    • @@ -207,7 +207,7 @@
  • Fluid >
    • -
  • Regularizer
    • +
  • regularizer
    • @@ -217,37 +217,55 @@
    -

    Regularizer

    -
    -

    WeightDecayRegularizer

    -
    +

    regularizer

    +
    +

    append_regularization_ops

    +
    -class paddle.v2.fluid.regularizer.WeightDecayRegularizer
    -

    Base class for weight decay regularizers

    -

    Defines the common interface of weight-decay regularizers. -Weight-decay regularizers are added only during the backward -pass for faster regularization. They add operations to the network -that correspond to gradient of the regularization function. -Users should not use this class directly, but need to use one -of its implementations

    +paddle.v2.fluid.regularizer.append_regularization_ops(parameters_and_grads, regularization=None) +

    Create and add backward regularization Operators

    +

    Creates and adds backward regularization operators in the BlockDesc. +This will add gradients of the regularizer function to the gradients +of the parameters and return these modified gradients. This is the +same as implementing weight decay in optimizers for regularization.

    + +++ + + + + + + + +
    Parameters:
      +
    • parameters_and_grads – A list of (parameters, gradients) pairs +that need to be regularized.
      • +
    • regularization – A global regularizer. If the parameter is not +set. It will be applied with regularizer.
      • +
    +
    Returns:

    list of (parameters, gradients) pair with the regularized gradient

    +
    Raises:

    Exception – Unknown regularization type

    +
    -
    -

    L2DecayRegularizer

    -
    +
    +

    L1Decay

    +
    -class paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
    -

    Implements the L2 Weight Decay Regularization

    +paddle.v2.fluid.regularizer.L1Decay +

    alias of L1DecayRegularizer

    -
    -

    L1DecayRegularizer

    -
    -
    -class paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)
    -

    Implements the L1 Weight Decay Regularization

    +
    +

    L2Decay

    +
    +
    +paddle.v2.fluid.regularizer.L2Decay
    +

    alias of L2DecayRegularizer

    @@ -260,10 +278,10 @@ of its implementations

    diff --git a/develop/doc/api/v2/model_configs.html b/develop/doc/api/v2/model_configs.html index 8042041351..29362778f3 100644 --- a/develop/doc/api/v2/model_configs.html +++ b/develop/doc/api/v2/model_configs.html @@ -162,17 +162,17 @@
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    • diff --git a/develop/doc/api/v2/run_logic.html b/develop/doc/api/v2/run_logic.html index f665bbb1b1..b1d3598a5a 100644 --- a/develop/doc/api/v2/run_logic.html +++ b/develop/doc/api/v2/run_logic.html @@ -162,17 +162,17 @@
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    • diff --git a/develop/doc/design/api.html b/develop/doc/design/api.html index 1903a81076..e1d938acfc 100644 --- a/develop/doc/design/api.html +++ b/develop/doc/design/api.html @@ -159,17 +159,17 @@
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    • @@ -229,11 +229,6 @@     		paddle
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112,tecton:126,templat:112,tensor:[4,18,79,87,103],tensorarrai:[69,89],tensordesc:90,tensorflow:56,test:[61,62,63,91,99,100,101,118,120],text_conv_pool:5,theori:27,thi:83,think:54,three:89,thresholded_relu:18,time:108,timelin:38,timer:107,tip:107,todo:[32,33,43],togeth:83,toler:31,tool:[30,104,107,109,126],toolchain:124,topic:87,topk:18,toward:52,train:[25,26,31,34,37,39,42,70,78,92,109,110,111,112,113,114,118,120],trainer:[25,31,36,38,39,41,109,112],tran:4,trans_full_matrix_project:4,transform:88,translat:69,transpil:[42,43,44,52,60,71],transpos:18,tune:[107,118],ture:51,two:27,type:[41,67,91,101],uci_h:10,uniform:[16,89],unit:[61,62,63,99,100,101,118],unpack:69,updat:[26,37,38,104,111,112],usag:[28,45,68,69,78,103,105],use:[34,78,103],user:31,using:98,util:3,valu:77,value_print:3,vardesc:90,variabl:[28,60,77,79,83,90],vector:118,verifi:112,version:[40,50,94],vgg_16_network:5,volum:112,vpc:112,warp_ctc:4,warpctc:18,weightnormparamattr:21,what:[34,38,98,107],when:[38,83],whileguard:18,whl:94,why:[50,51,72,78,79,89,107],wmt14:10,work:86,worker:40,workflow:99,wrapper:100,write:[99,100,101,102,104],www:104,xavier:16,yaml:113,your:[92,102],zero:18}}) \ No newline at end of file diff --git a/develop/doc/survey/cluster_bootstrapping_tools.html b/develop/doc/survey/cluster_bootstrapping_tools.html index 8a54bebca7..5286ba6b99 100644 --- a/develop/doc/survey/cluster_bootstrapping_tools.html +++ b/develop/doc/survey/cluster_bootstrapping_tools.html @@ -159,17 +159,17 @@
  • Training and Inference
  • Fluid
    • diff --git a/develop/doc_cn/_sources/api/v2/fluid/data_feeder.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/data_feeder.rst.txt index 0fa78f7dfb..a591c7334f 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/data_feeder.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/data_feeder.rst.txt @@ -1,9 +1,14 @@ +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + =========== -DataFeeder +data_feeder =========== DataFeeder ------------ -.. automodule:: paddle.v2.fluid.data_feeder - :members: DataFeeder +---------- + +.. autoclass:: paddle.v2.fluid.data_feeder.DataFeeder + :members: :noindex: + diff --git a/develop/doc_cn/_sources/api/v2/fluid/evaluator.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/evaluator.rst.txt index a23f3301d0..00dcecfd62 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/evaluator.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/evaluator.rst.txt @@ -1,9 +1,21 @@ -=========== -Evaluator -=========== - -Evaluator ------------ -.. automodule:: paddle.v2.fluid.evaluator - :members: Evaluator +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +========= +evaluator +========= + +Accuracy +-------- + +.. autoclass:: paddle.v2.fluid.evaluator.Accuracy + :members: :noindex: + +ChunkEvaluator +-------------- + +.. autoclass:: paddle.v2.fluid.evaluator.ChunkEvaluator + :members: + :noindex: + diff --git a/develop/doc_cn/_sources/api/v2/fluid/executor.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/executor.rst.txt index 3a283538c1..a028f6283f 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/executor.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/executor.rst.txt @@ -1,9 +1,32 @@ -=========== -Executor -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +======== +executor +======== Executor +-------- + +.. autoclass:: paddle.v2.fluid.executor.Executor + :members: + :noindex: + +global_scope +------------ + +.. autofunction:: paddle.v2.fluid.executor.global_scope + :noindex: + +scope_guard ----------- -.. automodule:: paddle.v2.fluid.executor - :members: Executor + +.. autofunction:: paddle.v2.fluid.executor.scope_guard + :noindex: + +switch_scope +------------ + +.. autofunction:: paddle.v2.fluid.executor.switch_scope :noindex: + diff --git a/develop/doc_cn/_sources/api/v2/fluid/initializer.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/initializer.rst.txt index 8f587837e9..c38be033ff 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/initializer.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/initializer.rst.txt @@ -1,50 +1,35 @@ +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + =========== -Initializer +initializer =========== +Constant +-------- - -Initializer ------------ -.. automodule:: paddle.v2.fluid.initializer - :members: Initializer - :noindex: - - - -ConstantInitializer -------------------- -.. automodule:: paddle.v2.fluid.initializer - :members: ConstantInitializer +.. autoclass:: paddle.v2.fluid.initializer.Constant + :members: :noindex: +Uniform +------- - -UniformInitializer ------------------- -.. automodule:: paddle.v2.fluid.initializer - :members: UniformInitializer - :noindex: - - - -NormalInitializer ------------------ -.. automodule:: paddle.v2.fluid.initializer - :members: NormalInitializer +.. autoclass:: paddle.v2.fluid.initializer.Uniform + :members: :noindex: +Normal +------ -XavierInitializer ------------------ -.. automodule:: paddle.v2.fluid.initializer - :members: XavierInitializer +.. autoclass:: paddle.v2.fluid.initializer.Normal + :members: :noindex: +Xavier +------ -MSRAInitializer ---------------- -.. automodule:: paddle.v2.fluid.initializer - :members: MSRAInitializer +.. autoclass:: paddle.v2.fluid.initializer.Xavier + :members: :noindex: diff --git a/develop/doc_cn/_sources/api/v2/fluid/io.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/io.rst.txt index 67f68c4e9e..37c9c273e3 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/io.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/io.rst.txt @@ -1,10 +1,61 @@ -=========== -IO -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +== +io +== +save_vars +--------- -is_parameter +.. autofunction:: paddle.v2.fluid.io.save_vars + :noindex: + +save_params ----------- -.. autofunction:: paddle.v2.fluid.io.is_parameter + +.. autofunction:: paddle.v2.fluid.io.save_params + :noindex: + +save_persistables +----------------- + +.. autofunction:: paddle.v2.fluid.io.save_persistables + :noindex: + +load_vars +--------- + +.. autofunction:: paddle.v2.fluid.io.load_vars + :noindex: + +load_params +----------- + +.. autofunction:: paddle.v2.fluid.io.load_params :noindex: + +load_persistables +----------------- + +.. autofunction:: paddle.v2.fluid.io.load_persistables + :noindex: + +save_inference_model +-------------------- + +.. autofunction:: paddle.v2.fluid.io.save_inference_model + :noindex: + +load_inference_model +-------------------- + +.. autofunction:: paddle.v2.fluid.io.load_inference_model + :noindex: + +get_inference_program +--------------------- + +.. autofunction:: paddle.v2.fluid.io.get_inference_program + :noindex: + diff --git a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt index 231ec2d4ba..e24613b94b 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt @@ -1,546 +1,799 @@ -========== -Layers -========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +====== +layers +====== -fc ---- -.. autofunction:: paddle.v2.fluid.layers.fc +control_flow +============ + +split_lod_tensor +---------------- + +.. autofunction:: paddle.v2.fluid.layers.split_lod_tensor :noindex: -embedding ---------- -.. autofunction:: paddle.v2.fluid.layers.embedding +merge_lod_tensor +---------------- + +.. autofunction:: paddle.v2.fluid.layers.merge_lod_tensor :noindex: -dynamic_lstm ------------- -.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm +BlockGuard +---------- + +.. autoclass:: paddle.v2.fluid.layers.BlockGuard + :members: :noindex: -dynamic_lstmp -------------- -.. autofunction:: paddle.v2.fluid.layers.dynamic_lstmp +BlockGuardWithCompletion +------------------------ + +.. autoclass:: paddle.v2.fluid.layers.BlockGuardWithCompletion + :members: :noindex: -dynamic_gru ------------ -.. autofunction:: paddle.v2.fluid.layers.dynamic_gru +StaticRNNMemoryLink +------------------- + +.. autoclass:: paddle.v2.fluid.layers.StaticRNNMemoryLink + :members: :noindex: -data ----- -.. autofunction:: paddle.v2.fluid.layers.data +WhileGuard +---------- + +.. autoclass:: paddle.v2.fluid.layers.WhileGuard + :members: :noindex: -mean ----- -.. autofunction:: paddle.v2.fluid.layers.mean +While +----- + +.. autoclass:: paddle.v2.fluid.layers.While + :members: :noindex: -mul ---- -.. autofunction:: paddle.v2.fluid.layers.mul +lod_rank_table +-------------- + +.. autofunction:: paddle.v2.fluid.layers.lod_rank_table :noindex: -elementwise_add ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_add +max_sequence_len +---------------- + +.. autofunction:: paddle.v2.fluid.layers.max_sequence_len :noindex: -elementwise_sub ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_sub +topk +---- + +.. autofunction:: paddle.v2.fluid.layers.topk :noindex: -elementwise_mul ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_mul +lod_tensor_to_array +------------------- + +.. autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array :noindex: -elementwise_div ---------------- -.. autofunction:: paddle.v2.fluid.layers.elementwise_div +array_to_lod_tensor +------------------- + +.. autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor :noindex: +increment +--------- -dropout -------- -.. autofunction:: paddle.v2.fluid.layers.dropout +.. autofunction:: paddle.v2.fluid.layers.increment :noindex: +array_write +----------- -reshape --------- -.. autofunction:: paddle.v2.fluid.layers.reshape +.. autofunction:: paddle.v2.fluid.layers.array_write :noindex: +create_array +------------ -sigmoid +.. autofunction:: paddle.v2.fluid.layers.create_array + :noindex: + +less_than --------- -.. autofunction:: paddle.v2.fluid.layers.sigmoid + +.. autofunction:: paddle.v2.fluid.layers.less_than :noindex: +array_read +---------- -scale ---------- -.. autofunction:: paddle.v2.fluid.layers.scale +.. autofunction:: paddle.v2.fluid.layers.array_read + :noindex: + +shrink_memory +------------- + +.. autofunction:: paddle.v2.fluid.layers.shrink_memory :noindex: +array_length +------------ -transpose +.. autofunction:: paddle.v2.fluid.layers.array_length + :noindex: + +IfElse +------ + +.. autoclass:: paddle.v2.fluid.layers.IfElse + :members: + :noindex: + +DynamicRNN +---------- + +.. autoclass:: paddle.v2.fluid.layers.DynamicRNN + :members: + :noindex: + +ConditionalBlock +---------------- + +.. autoclass:: paddle.v2.fluid.layers.ConditionalBlock + :members: + :noindex: + +StaticRNN --------- -.. autofunction:: paddle.v2.fluid.layers.transpose + +.. autoclass:: paddle.v2.fluid.layers.StaticRNN + :members: :noindex: +reorder_lod_tensor_by_rank +-------------------------- -sigmoid_cross_entropy_with_logits ---------------------------------- -.. autofunction:: paddle.v2.fluid.layers.esigmoid_cross_entropy_with_logits +.. autofunction:: paddle.v2.fluid.layers.reorder_lod_tensor_by_rank :noindex: +ParallelDo +---------- -cast +.. autoclass:: paddle.v2.fluid.layers.ParallelDo + :members: + :noindex: + +Print +----- + +.. autofunction:: paddle.v2.fluid.layers.Print + :noindex: + +device +====== + +get_places +---------- + +.. autofunction:: paddle.v2.fluid.layers.get_places + :noindex: + +io +== + +data ---- -.. autofunction:: paddle.v2.fluid.layers.cast + +.. autofunction:: paddle.v2.fluid.layers.data :noindex: +BlockGuardServ +-------------- -concat -------- -.. autofunction:: paddle.v2.fluid.layers.concat +.. autoclass:: paddle.v2.fluid.layers.BlockGuardServ + :members: :noindex: +ListenAndServ +------------- -sums +.. autoclass:: paddle.v2.fluid.layers.ListenAndServ + :members: + :noindex: + +Send ---- -.. autofunction:: paddle.v2.fluid.layers.sums + +.. autofunction:: paddle.v2.fluid.layers.Send :noindex: +nn +== -linear_chain_crf ----------------- -.. autofunction:: paddle.v2.fluid.layers.linear_chain_crf +fc +-- + +.. autofunction:: paddle.v2.fluid.layers.fc :noindex: +embedding +--------- -assign -------- .. autofunction:: paddle.v2.fluid.layers.embedding :noindex: +dynamic_lstm +------------ -split_lod_tensor ----------------- -.. autofunction:: paddle.v2.fluid.layers.split_lod_tensor +.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm :noindex: +dynamic_lstmp +------------- -merge_lod_tensor +.. autofunction:: paddle.v2.fluid.layers.dynamic_lstmp + :noindex: + +dynamic_gru +----------- + +.. autofunction:: paddle.v2.fluid.layers.dynamic_gru + :noindex: + +gru_unit +-------- + +.. autofunction:: paddle.v2.fluid.layers.gru_unit + :noindex: + +linear_chain_crf ---------------- -.. autofunction:: paddle.v2.fluid.layers.merge_lod_tensor + +.. autofunction:: paddle.v2.fluid.layers.linear_chain_crf + :noindex: + +crf_decoding +------------ + +.. autofunction:: paddle.v2.fluid.layers.crf_decoding :noindex: cos_sim --------- +------- + .. autofunction:: paddle.v2.fluid.layers.cos_sim :noindex: - cross_entropy ------------- + .. autofunction:: paddle.v2.fluid.layers.cross_entropy :noindex: - - square_error_cost ----------------- + .. autofunction:: paddle.v2.fluid.layers.square_error_cost :noindex: - accuracy ---------- +-------- + .. autofunction:: paddle.v2.fluid.layers.accuracy :noindex: +chunk_eval +---------- + +.. autofunction:: paddle.v2.fluid.layers.chunk_eval + :noindex: sequence_conv ------------- + .. autofunction:: paddle.v2.fluid.layers.sequence_conv :noindex: - conv2d ------ + .. autofunction:: paddle.v2.fluid.layers.conv2d :noindex: - sequence_pool ------------- + .. autofunction:: paddle.v2.fluid.layers.sequence_pool :noindex: +pool2d +------ -sequence_first_step -------------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_first_step +.. autofunction:: paddle.v2.fluid.layers.pool2d :noindex: +batch_norm +---------- + +.. autofunction:: paddle.v2.fluid.layers.batch_norm + :noindex: -sequence_last_step +beam_search_decode ------------------ -.. autofunction:: paddle.v2.fluid.layers.sequence_last_step + +.. autofunction:: paddle.v2.fluid.layers.beam_search_decode :noindex: +conv2d_transpose +---------------- -pool2d ------- -.. autofunction:: paddle.v2.fluid.layers.pool2d +.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose :noindex: +sequence_expand +--------------- -batch_norm +.. autofunction:: paddle.v2.fluid.layers.sequence_expand + :noindex: + +lstm_unit +--------- + +.. autofunction:: paddle.v2.fluid.layers.lstm_unit + :noindex: + +reduce_sum ---------- -.. autofunction:: paddle.v2.fluid.layers.batch_norm + +.. autofunction:: paddle.v2.fluid.layers.reduce_sum + :noindex: + +reduce_mean +----------- + +.. autofunction:: paddle.v2.fluid.layers.reduce_mean :noindex: +reduce_max +---------- + +.. autofunction:: paddle.v2.fluid.layers.reduce_max + :noindex: -beam_search_decode +reduce_min +---------- + +.. autofunction:: paddle.v2.fluid.layers.reduce_min + :noindex: + +sequence_first_step +------------------- + +.. autofunction:: paddle.v2.fluid.layers.sequence_first_step + :noindex: + +sequence_last_step ------------------ -.. autofunction:: paddle.v2.fluid.layers.beam_search_decode + +.. autofunction:: paddle.v2.fluid.layers.sequence_last_step + :noindex: + +dropout +------- + +.. autofunction:: paddle.v2.fluid.layers.dropout :noindex: +split +----- -lod_rank_table --------------- -.. autofunction:: paddle.v2.fluid.layers.lod_rank_table +.. autofunction:: paddle.v2.fluid.layers.split :noindex: +ctc_greedy_decoder +------------------ -max_sequence_len ----------------- -.. autofunction:: paddle.v2.fluid.layers.max_sequence_len +.. autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder :noindex: +edit_distance +------------- -topk ------ -.. autofunction:: paddle.v2.fluid.layers.topk +.. autofunction:: paddle.v2.fluid.layers.edit_distance :noindex: +l2_normalize +------------ -lod_tensor_to_array -------------------- -.. autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array +.. autofunction:: paddle.v2.fluid.layers.l2_normalize :noindex: +matmul +------ - -array_to_lod_tensor -------------------- -.. autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor +.. autofunction:: paddle.v2.fluid.layers.matmul :noindex: +warpctc +------- +.. autofunction:: paddle.v2.fluid.layers.warpctc + :noindex: +sequence_reshape +---------------- -fill_constant -------------- -.. autofunction:: paddle.v2.fluid.layers.fill_constant +.. autofunction:: paddle.v2.fluid.layers.sequence_reshape :noindex: +transpose +--------- +.. autofunction:: paddle.v2.fluid.layers.transpose + :noindex: -fill_constant_batch_size_like ------------------------------ -.. autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like +im2sequence +----------- + +.. autofunction:: paddle.v2.fluid.layers.im2sequence :noindex: +nce +--- -ones ----- -.. autofunction:: paddle.v2.fluid.layers.ones +.. autofunction:: paddle.v2.fluid.layers.nce :noindex: +beam_search +----------- -zeros ------ -.. autofunction:: paddle.v2.fluid.layers.zeros +.. autofunction:: paddle.v2.fluid.layers.beam_search :noindex: +row_conv +-------- -increment ---------- -.. autofunction:: paddle.v2.fluid.layers.increment +.. autofunction:: paddle.v2.fluid.layers.row_conv :noindex: +multiplex +--------- -array_write ------------ -.. autofunction:: paddle.v2.fluid.layers.array_write +.. autofunction:: paddle.v2.fluid.layers.multiplex :noindex: +ops +=== +mean +---- -create_array ------------- -.. autofunction:: paddle.v2.fluid.layers.create_array +.. autofunction:: paddle.v2.fluid.layers.mean :noindex: +mul +--- -less_than ---------- -.. autofunction:: paddle.v2.fluid.layers.less_than +.. autofunction:: paddle.v2.fluid.layers.mul :noindex: +reshape +------- -array_read ----------- -.. autofunction:: paddle.v2.fluid.layers.array_read +.. autofunction:: paddle.v2.fluid.layers.reshape :noindex: +scale +----- -shrink_memory --------------- -.. autofunction:: paddle.v2.fluid.layers.shrink_memory +.. autofunction:: paddle.v2.fluid.layers.scale :noindex: +sigmoid_cross_entropy_with_logits +--------------------------------- -array_length -------------- -.. autofunction:: paddle.v2.fluid.layers.array_length +.. autofunction:: paddle.v2.fluid.layers.sigmoid_cross_entropy_with_logits :noindex: +elementwise_add +--------------- -conv2d_transpose ----------------- -.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose +.. autofunction:: paddle.v2.fluid.layers.elementwise_add :noindex: - -sequence_expand +elementwise_div --------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_expand + +.. autofunction:: paddle.v2.fluid.layers.elementwise_div :noindex: +elementwise_sub +--------------- -gru_unit --------- -.. autofunction:: paddle.v2.fluid.layers.gru_unit +.. autofunction:: paddle.v2.fluid.layers.elementwise_sub :noindex: +elementwise_mul +--------------- -lstm_unit ---------- -.. autofunction:: paddle.v2.fluid.layers.lstm_unit +.. autofunction:: paddle.v2.fluid.layers.elementwise_mul :noindex: +elementwise_max +--------------- -sequence_softmax ----------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_softmax +.. autofunction:: paddle.v2.fluid.layers.elementwise_max :noindex: +elementwise_min +--------------- -reduce_sum ----------- -.. autofunction:: paddle.v2.fluid.layers.reduce_sum +.. autofunction:: paddle.v2.fluid.layers.elementwise_min :noindex: +elementwise_pow +--------------- -reduce_mean ------------ -.. autofunction:: paddle.v2.fluid.layers.reduce_mean +.. autofunction:: paddle.v2.fluid.layers.elementwise_pow :noindex: +clip +---- -reduce_max ----------- -.. autofunction:: paddle.v2.fluid.layers.reduce_max +.. autofunction:: paddle.v2.fluid.layers.clip :noindex: +clip_by_norm +------------ -reduce_min ----------- -.. autofunction:: paddle.v2.fluid.layers.reduce_min +.. autofunction:: paddle.v2.fluid.layers.clip_by_norm :noindex: +sequence_softmax +---------------- -split ------ -.. autofunction:: paddle.v2.fluid.layers.split +.. autofunction:: paddle.v2.fluid.layers.sequence_softmax :noindex: +sigmoid +------- -matmul ------- -.. autofunction:: paddle.v2.fluid.layers.matmul +.. autofunction:: paddle.v2.fluid.layers.sigmoid :noindex: logsigmoid ---------- + .. autofunction:: paddle.v2.fluid.layers.logsigmoid :noindex: exp --- + .. autofunction:: paddle.v2.fluid.layers.exp :noindex: relu ---- + .. autofunction:: paddle.v2.fluid.layers.relu :noindex: tanh ---- + .. autofunction:: paddle.v2.fluid.layers.tanh :noindex: tanh_shrink ----------- + .. autofunction:: paddle.v2.fluid.layers.tanh_shrink :noindex: softshrink ---------- + .. autofunction:: paddle.v2.fluid.layers.softshrink :noindex: sqrt ---- + .. autofunction:: paddle.v2.fluid.layers.sqrt :noindex: abs ----- +--- + .. autofunction:: paddle.v2.fluid.layers.abs :noindex: ceil ---- + .. autofunction:: paddle.v2.fluid.layers.ceil :noindex: floor ----- + .. autofunction:: paddle.v2.fluid.layers.floor :noindex: round ----- + .. autofunction:: paddle.v2.fluid.layers.round :noindex: reciprocal ---------- + .. autofunction:: paddle.v2.fluid.layers.reciprocal :noindex: log --- + .. autofunction:: paddle.v2.fluid.layers.log :noindex: square ------ + .. autofunction:: paddle.v2.fluid.layers.square :noindex: softplus -------- + .. autofunction:: paddle.v2.fluid.layers.softplus :noindex: softsign ---------- +-------- + .. autofunction:: paddle.v2.fluid.layers.softsign :noindex: brelu ----- + .. autofunction:: paddle.v2.fluid.layers.brelu :noindex: leaky_relu ---------- + .. autofunction:: paddle.v2.fluid.layers.leaky_relu :noindex: soft_relu --------- + .. autofunction:: paddle.v2.fluid.layers.soft_relu :noindex: elu ----- +--- + .. autofunction:: paddle.v2.fluid.layers.elu :noindex: relu6 ----- + .. autofunction:: paddle.v2.fluid.layers.relu6 :noindex: pow ----- +--- + .. autofunction:: paddle.v2.fluid.layers.pow :noindex: +stanh +----- + +.. autofunction:: paddle.v2.fluid.layers.stanh + :noindex: + hard_shrink ----------- + .. autofunction:: paddle.v2.fluid.layers.hard_shrink :noindex: thresholded_relu ---------------- + .. autofunction:: paddle.v2.fluid.layers.thresholded_relu :noindex: hard_sigmoid -------------- +------------ + .. autofunction:: paddle.v2.fluid.layers.hard_sigmoid :noindex: swish ------- +----- + .. autofunction:: paddle.v2.fluid.layers.swish :noindex: -im2sequence +tensor +====== + +create_tensor +------------- + +.. autofunction:: paddle.v2.fluid.layers.create_tensor + :noindex: + +create_parameter +---------------- + +.. autofunction:: paddle.v2.fluid.layers.create_parameter + :noindex: + +create_global_var +----------------- + +.. autofunction:: paddle.v2.fluid.layers.create_global_var + :noindex: + +cast +---- + +.. autofunction:: paddle.v2.fluid.layers.cast + :noindex: + +concat ------ -.. autofunction:: paddle.v2.fluid.layers.im2sequence + +.. autofunction:: paddle.v2.fluid.layers.concat :noindex: -edit_distance ---------------- -.. autofunction:: paddle.v2.fluid.layers.edit_distance_error +sums +---- + +.. autofunction:: paddle.v2.fluid.layers.sums :noindex: -ctc_greedy_decoder ---------------- -.. autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder +assign +------ + +.. autofunction:: paddle.v2.fluid.layers.assign :noindex: -l2_normalize ------------- -.. autofunction:: paddle.v2.fluid.layers.l2_normalize +fill_constant_batch_size_like +----------------------------- + +.. autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like :noindex: -sequence_reshape ----------------- -.. autofunction:: paddle.v2.fluid.layers.sequence_reshape +fill_constant +------------- + +.. autofunction:: paddle.v2.fluid.layers.fill_constant :noindex: -row_conv --------- -.. autofunction:: paddle.v2.fluid.layers.row_conv +ones +---- + +.. autofunction:: paddle.v2.fluid.layers.ones :noindex: -multiplex ---------- -.. autofunction:: paddle.v2.fluid.layers.multiplex +zeros +----- + +.. autofunction:: paddle.v2.fluid.layers.zeros :noindex: + diff --git a/develop/doc_cn/_sources/api/v2/fluid/nets.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/nets.rst.txt index 500019bc50..015581b766 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/nets.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/nets.rst.txt @@ -1,33 +1,31 @@ -=========== -Nets -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +==== +nets +==== simple_img_conv_pool -------------------- -.. autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool - :noindex: - -img_conv_group ---------------- -.. autofunction:: paddle.v2.fluid.nets.img_conv_group +.. autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool :noindex: - sequence_conv_pool ------------------ + .. autofunction:: paddle.v2.fluid.nets.sequence_conv_pool :noindex: - glu --- + .. autofunction:: paddle.v2.fluid.nets.glu :noindex: - scaled_dot_product_attention ---------------------------- + .. autofunction:: paddle.v2.fluid.nets.scaled_dot_product_attention :noindex: diff --git a/develop/doc_cn/_sources/api/v2/fluid/optimizer.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/optimizer.rst.txt index 19b4940f08..1691ebb9a7 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/optimizer.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/optimizer.rst.txt @@ -1,54 +1,49 @@ -=========== -Optimizer -=========== - -Optimizer ------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: Optimizer - :noindex: +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +========= +optimizer +========= -SGDOptimizer ------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: SGDOptimizer - :noindex: +SGD +--- +.. autoclass:: paddle.v2.fluid.optimizer.SGD + :members: + :noindex: +Momentum +-------- -MomentumOptimizer ------------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: MomentumOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Momentum + :members: :noindex: +Adagrad +------- - -AdagradOptimizer ----------------- -.. automodule:: paddle.v2.fluid.optimizer - :members: AdagradOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Adagrad + :members: :noindex: +Adam +---- -AdamOptimizer -------------- -.. automodule:: paddle.v2.fluid.optimizer - :members: AdamOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Adam + :members: :noindex: +Adamax +------ -AdamaxOptimizer ------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: AdamaxOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.Adamax + :members: :noindex: +DecayedAdagrad +-------------- -DecayedAdagradOptimizer ------------------------ -.. automodule:: paddle.v2.fluid.optimizer - :members: DecayedAdagradOptimizer +.. autoclass:: paddle.v2.fluid.optimizer.DecayedAdagrad + :members: :noindex: diff --git a/develop/doc_cn/_sources/api/v2/fluid/param_attr.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/param_attr.rst.txt index ca0c8af9e8..8083d0d858 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/param_attr.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/param_attr.rst.txt @@ -1,11 +1,21 @@ -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + +========== +param_attr +========== + ParamAttr -=========== +--------- +.. autoclass:: paddle.v2.fluid.param_attr.ParamAttr + :members: + :noindex: +WeightNormParamAttr +------------------- -ParamAttr ------------ -.. automodule:: paddle.v2.fluid.param_attr - :members: ParamAttr +.. autoclass:: paddle.v2.fluid.param_attr.WeightNormParamAttr + :members: :noindex: + diff --git a/develop/doc_cn/_sources/api/v2/fluid/profiler.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/profiler.rst.txt index 7d4042d1f4..4a1ff7cb69 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/profiler.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/profiler.rst.txt @@ -1,10 +1,25 @@ -=========== -Profiler -=========== +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! +======== +profiler +======== +cuda_profiler +------------- -Profiler ------------ .. autofunction:: paddle.v2.fluid.profiler.cuda_profiler :noindex: + +reset_profiler +-------------- + +.. autofunction:: paddle.v2.fluid.profiler.reset_profiler + :noindex: + +profiler +-------- + +.. autofunction:: paddle.v2.fluid.profiler.profiler + :noindex: + diff --git a/develop/doc_cn/_sources/api/v2/fluid/regularizer.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/regularizer.rst.txt index 868e225ed3..2c17d15599 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/regularizer.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/regularizer.rst.txt @@ -1,25 +1,27 @@ +.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}` + !DO NOT EDIT THIS FILE MANUALLY! + =========== -Regularizer +regularizer =========== -WeightDecayRegularizer ----------------------- -.. automodule:: paddle.v2.fluid.regularizer - :members: WeightDecayRegularizer - :noindex: - +append_regularization_ops +------------------------- -L2DecayRegularizer ------------------- -.. automodule:: paddle.v2.fluid.regularizer - :members: L2DecayRegularizer +.. autofunction:: paddle.v2.fluid.regularizer.append_regularization_ops :noindex: +L1Decay +------- +.. autoclass:: paddle.v2.fluid.regularizer.L1Decay + :members: + :noindex: -L1DecayRegularizer -------------------- -.. automodule:: paddle.v2.fluid.regularizer - :members: L1DecayRegularizer +L2Decay +------- +.. autoclass:: paddle.v2.fluid.regularizer.L2Decay + :members: + :noindex: diff --git a/develop/doc_cn/api/index_cn.html b/develop/doc_cn/api/index_cn.html index e630b53aeb..92ad7605dd 100644 --- a/develop/doc_cn/api/index_cn.html +++ b/develop/doc_cn/api/index_cn.html @@ -172,17 +172,17 @@
  • 训练与应用
  • Fluid
    • diff --git a/develop/doc_cn/api/v2/config/activation.html b/develop/doc_cn/api/v2/config/activation.html index b4e9d3d8e4..55bc0e3171 100644 --- a/develop/doc_cn/api/v2/config/activation.html +++ b/develop/doc_cn/api/v2/config/activation.html @@ -173,17 +173,17 @@
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    • diff --git a/develop/doc_cn/api/v2/config/attr.html b/develop/doc_cn/api/v2/config/attr.html index f4118fe1c2..84bcd0e6f2 100644 --- a/develop/doc_cn/api/v2/config/attr.html +++ b/develop/doc_cn/api/v2/config/attr.html @@ -173,17 +173,17 @@
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    • diff --git a/develop/doc_cn/api/v2/config/evaluators.html b/develop/doc_cn/api/v2/config/evaluators.html index 6a3dd7b7ff..110f99429d 100644 --- a/develop/doc_cn/api/v2/config/evaluators.html +++ b/develop/doc_cn/api/v2/config/evaluators.html @@ -173,17 +173,17 @@
  • 训练与应用
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    • diff --git a/develop/doc_cn/api/v2/config/layer.html b/develop/doc_cn/api/v2/config/layer.html index d2fda21af9..87ee264489 100644 --- a/develop/doc_cn/api/v2/config/layer.html +++ b/develop/doc_cn/api/v2/config/layer.html @@ -173,17 +173,17 @@
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    • diff --git a/develop/doc_cn/api/v2/config/networks.html b/develop/doc_cn/api/v2/config/networks.html index 6435474c51..d97762c6ee 100644 --- a/develop/doc_cn/api/v2/config/networks.html +++ b/develop/doc_cn/api/v2/config/networks.html @@ -173,17 +173,17 @@
  • 训练与应用
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    • diff --git a/develop/doc_cn/api/v2/config/optimizer.html b/develop/doc_cn/api/v2/config/optimizer.html index 2010c91abc..8391ad321f 100644 --- a/develop/doc_cn/api/v2/config/optimizer.html +++ b/develop/doc_cn/api/v2/config/optimizer.html @@ -173,17 +173,17 @@
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    • diff --git a/develop/doc_cn/api/v2/config/pooling.html b/develop/doc_cn/api/v2/config/pooling.html index a8aa37b0f5..3dcaa73592 100644 --- a/develop/doc_cn/api/v2/config/pooling.html +++ b/develop/doc_cn/api/v2/config/pooling.html @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • diff --git a/develop/doc_cn/api/v2/data.html b/develop/doc_cn/api/v2/data.html index 400ae40c63..7a7a9d88c4 100644 --- a/develop/doc_cn/api/v2/data.html +++ b/develop/doc_cn/api/v2/data.html @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • diff --git a/develop/doc_cn/api/v2/data/data_reader.html b/develop/doc_cn/api/v2/data/data_reader.html index 4ff1e824ed..683de17006 100644 --- a/develop/doc_cn/api/v2/data/data_reader.html +++ b/develop/doc_cn/api/v2/data/data_reader.html @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • diff --git a/develop/doc_cn/api/v2/data/dataset.html b/develop/doc_cn/api/v2/data/dataset.html index 634ab6b609..0243e7ae1c 100644 --- a/develop/doc_cn/api/v2/data/dataset.html +++ b/develop/doc_cn/api/v2/data/dataset.html @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • diff --git a/develop/doc_cn/api/v2/data/image.html b/develop/doc_cn/api/v2/data/image.html index 271b20e7f4..20298b47d8 100644 --- a/develop/doc_cn/api/v2/data/image.html +++ b/develop/doc_cn/api/v2/data/image.html @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • diff --git a/develop/doc_cn/api/v2/fluid.html b/develop/doc_cn/api/v2/fluid.html index c90e71492d..acc7de0a9e 100644 --- a/develop/doc_cn/api/v2/fluid.html +++ b/develop/doc_cn/api/v2/fluid.html @@ -34,7 +34,7 @@ - + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -237,17 +237,17 @@

    Fluid

    @@ -259,7 +259,7 @@ @@ -235,10 +235,15 @@
    -
    -

    DataFeeder

    -
    -

    DataFeeder

    +
    +

    data_feeder

    +
    +

    DataFeeder

    +
    +
    +class paddle.v2.fluid.data_feeder.DataFeeder(feed_list, place, program=None)
    +
    +
    @@ -249,10 +254,10 @@ diff --git a/develop/doc_cn/api/v2/fluid/evaluator.html b/develop/doc_cn/api/v2/fluid/evaluator.html index 0054570a76..3c964240fb 100644 --- a/develop/doc_cn/api/v2/fluid/evaluator.html +++ b/develop/doc_cn/api/v2/fluid/evaluator.html @@ -8,7 +8,7 @@ - Evaluator — PaddlePaddle 文档 + evaluator — PaddlePaddle 文档 @@ -34,8 +34,8 @@ - - + + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • Evaluator
    • +
  • evaluator
    • @@ -236,76 +236,24 @@
    -

    Evaluator

    -
    -

    Evaluator

    +

    evaluator

    +
    +

    Accuracy

    -class paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)
    -

    Base Class for all evaluators

    - --- - - - -
    参数:
      -
    • name (str) – The name of evaluator. such as, “accuracy”. Used for generate -temporary variable name.
      • -
    • main_program (Program, optional) – The evaluator should be added to this -main_program. Default default_main_program()
      • -
    • startup_program (Program, optional) – The parameter should be added to this -startup_program. Default default_startup_program()
      • -
    -
    -
    -
    -states
    -

    list – The list of state variables. states will be reset to zero -when reset is invoked.

    -
    - -
    -
    -metrics
    -

    list – The list of metrics variables. They will be calculate -every mini-batch

    -
    - -
    -
    -reset(executor, reset_program=None)
    -

    reset metric states at the begin of each pass/user specified batch

    -
    - -
    -
    -eval(executor, eval_program=None)
    -

    Evaluate the statistics merged by multiple mini-batches.

    +class paddle.v2.fluid.evaluator.Accuracy(input, label, k=1, **kwargs) +

    Average Accuracy for multiple mini-batches.

    -
    +
    +
    +

    ChunkEvaluator

    +
    -create_state(suffix, dtype, shape)
    -

    Create state variable.

    -

    NOTE: It is not a public API.

    - --- - - - -
    参数:
      -
    • suffix (str) – the state suffix.
      • -
    • dtype (str|core.DataType) – the state data type
      • -
    • shape (tuple|list) – the shape of state
      • -
    -
    -

    Returns: State variable

    -
    - +class paddle.v2.fluid.evaluator.ChunkEvaluator(input, label, chunk_scheme, num_chunk_types, excluded_chunk_types=None) +

    Accumulate counter numbers output by chunk_eval from mini-batches and +compute the precision recall and F1-score using the accumulated counter +numbers.

    @@ -318,10 +266,10 @@ every mini-batch

    diff --git a/develop/doc_cn/api/v2/fluid/executor.html b/develop/doc_cn/api/v2/fluid/executor.html index 569e950f28..d1c95576c3 100644 --- a/develop/doc_cn/api/v2/fluid/executor.html +++ b/develop/doc_cn/api/v2/fluid/executor.html @@ -8,7 +8,7 @@ - Executor — PaddlePaddle 文档 + executor — PaddlePaddle 文档 @@ -34,8 +34,8 @@ - - + + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • Executor
    • +
  • executor
    • @@ -236,9 +236,38 @@
    -

    Executor

    +

    executor

    Executor

    +
    +
    +class paddle.v2.fluid.executor.Executor(places)
    +
    + +
    +
    +

    global_scope

    +
    +
    +paddle.v2.fluid.executor.global_scope()
    +
    + +
    +
    +

    scope_guard

    +
    +
    +paddle.v2.fluid.executor.scope_guard(*args, **kwds)
    +
    + +
    +
    +

    switch_scope

    +
    +
    +paddle.v2.fluid.executor.switch_scope(scope)
    +
    +
    @@ -249,10 +278,10 @@ diff --git a/develop/doc_cn/api/v2/fluid/initializer.html b/develop/doc_cn/api/v2/fluid/initializer.html index 8427e9a108..d302e002ec 100644 --- a/develop/doc_cn/api/v2/fluid/initializer.html +++ b/develop/doc_cn/api/v2/fluid/initializer.html @@ -8,7 +8,7 @@ - Initializer — PaddlePaddle 文档 + initializer — PaddlePaddle 文档 @@ -34,8 +34,8 @@ - - + + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • Initializer
    • +
  • initializer
    • @@ -236,91 +236,40 @@
    -

    Initializer

    -
    -

    Initializer

    -
    +

    initializer

    +
    +

    Constant

    +
    -class paddle.v2.fluid.initializer.Initializer
    -

    Base class for variable initializers

    -

    Defines the common interface of variable initializers. -They add operations to the init program that are used -to initialize variables. Users should not use this class -directly, but need to use one of its implementations.

    +paddle.v2.fluid.initializer.Constant +

    ConstantInitializer 的别名

    -
    -

    ConstantInitializer

    -
    +
    +

    Uniform

    +
    -class paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)
    -

    Implements the constant initializer

    +paddle.v2.fluid.initializer.Uniform +

    UniformInitializer 的别名

    -
    -

    UniformInitializer

    -
    +
    +

    Normal

    +
    -class paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)
    -

    Implements the random uniform distribution initializer

    +paddle.v2.fluid.initializer.Normal +

    NormalInitializer 的别名

    -
    -

    NormalInitializer

    -
    +
    +

    Xavier

    +
    -class paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)
    -

    Implements the random Normal(Gaussian) distribution initializer

    -
    - -
    -
    -

    XavierInitializer

    -
    -
    -class paddle.v2.fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0)
    -

    Implements the Xavier initializer

    -

    This class implements the Xavier weight initializer from the paper -Understanding the difficulty of training deep feedforward neural -networks[1] by Xavier Glorot and Yoshua Bengio.

    -

    This initializer is designed to keep the scale of the gradients -approximately same in all the layers. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)). -In case of Normal distribution, the mean is 0 and the standard deviation -is sqrt(2/ (fan_in + fan_out)).

    -

    References

    -
    -
    [1] Understanding the difficulty of training deep feedforward neural
    -
    networks. International conference on artificial intelligence and -statistics. -(http://proceedings.mlr.press/v9/glorot10a.html)
    -
    -
    - -
    -
    -

    MSRAInitializer

    -
    -
    -class paddle.v2.fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0)
    -

    Implements the MSRA initializer a.k.a. Kaiming Initializer

    -

    This class implements the weight initialization from the paper -Delving Deep into Rectifiers: Surpassing Human-Level Performance on -ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren -and Jian Sun. This is a robust initialization method that particularly -considers the rectifier nonlinearities. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal -distribution, the mean is 0 and the standard deviation -is sqrt(2/ fan_in).

    -

    References

    -
    -
    [1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
    -
    on ImageNet Classification -(https://arxiv.org/abs/1502.01852)
    -
    +paddle.v2.fluid.initializer.Xavier +

    XavierInitializer 的别名

    @@ -333,10 +282,10 @@ is sqrt(2/ fan_in).

    diff --git a/develop/doc_cn/api/v2/fluid/io.html b/develop/doc_cn/api/v2/fluid/io.html index a68b6e9425..0a0c51c9f5 100644 --- a/develop/doc_cn/api/v2/fluid/io.html +++ b/develop/doc_cn/api/v2/fluid/io.html @@ -8,7 +8,7 @@ - IO — PaddlePaddle 文档 + io — PaddlePaddle 文档 @@ -35,7 +35,7 @@ - + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • IO
    • +
  • io
    • @@ -236,26 +236,162 @@
    -

    IO

    -
    -

    is_parameter

    +

    io

    +
    +

    save_vars

    -paddle.v2.fluid.io.is_parameter(var)
    -

    Check whether the variable is a Parameter.

    -

    This function checks whether the input variable is a Parameter.

    +paddle.v2.fluid.io.save_vars(executor, dirname, main_program=None, vars=None, predicate=None) +

    Save variables to directory by executor.

    - + - + +
    参数:var – The input variable.
    参数:
      +
    • executor – executor that save variable
      • +
    • dirname – directory path
      • +
    • main_program – program. If vars is None, then filter all variables in this
      • +
    +
    返回:boolean result whether the variable is a Parameter.
    +

    program which fit predicate. Default default_main_program. +:param predicate: The Predicate describes a callable that returns a variable +as a bool. If it returns true, the variables will be saved. +:param vars: variables need to be saved. If specify vars, program & predicate +will be ignored +:return: None

    +
    + +
    +
    +

    save_params

    +
    +
    +paddle.v2.fluid.io.save_params(executor, dirname, main_program=None)
    +

    Save all parameters to directory with executor.

    +
    + +
    +
    +

    save_persistables

    +
    +
    +paddle.v2.fluid.io.save_persistables(executor, dirname, main_program=None)
    +

    Save all persistables to directory with executor.

    +
    + +
    +
    +

    load_vars

    +
    +
    +paddle.v2.fluid.io.load_vars(executor, dirname, main_program=None, vars=None, predicate=None)
    +

    Load variables from directory by executor.

    + +++ +
    参数:
      +
    • executor – executor that save variable
      • +
    • dirname – directory path
      • +
    • main_program – program. If vars is None, then filter all variables in this
      • +
    +
    +

    program which fit predicate. Default default_main_program(). +:param predicate: The Predicate describes a callable that returns a variable +as a bool. If it returns true, the variables will be loaded. +:param vars: variables need to be loaded. If specify vars, program & +predicate will be ignored +:return: None

    +
    + +
    +
    +

    load_params

    +
    +
    +paddle.v2.fluid.io.load_params(executor, dirname, main_program=None)
    +

    load all parameters from directory by executor.

    +
    + +
    +
    +

    load_persistables

    +
    +
    +paddle.v2.fluid.io.load_persistables(executor, dirname, main_program=None)
    +

    load all persistables from directory by executor.

    +
    +
    +

    save_inference_model

    +
    +
    +paddle.v2.fluid.io.save_inference_model(dirname, feeded_var_names, target_vars, executor, main_program=None)
    +

    Build a model especially for inference, +and save it to directory by the executor.

    + +++ + + + + + +
    参数:
      +
    • dirname – directory path
      • +
    • feeded_var_names – Names of variables that need to be feeded data during inference
      • +
    • target_vars – Variables from which we can get inference results.
      • +
    • executor – executor that save inference model
      • +
    • main_program – original program, which will be pruned to build the inference model. +Default default_main_program().
      • +
    +
    返回:

    None

    +
    +
    + +
    +
    +

    load_inference_model

    +
    +
    +paddle.v2.fluid.io.load_inference_model(dirname, executor)
    +

    Load inference model from a directory

    + +++ + + + + + +
    参数:
      +
    • dirname – directory path
      • +
    • executor – executor that load inference model
      • +
    +
    返回:

    [program, feed_target_names, fetch_targets] +program: program especially for inference. +feed_target_names: Names of variables that need to feed data +fetch_targets: Variables from which we can get inference results.

    +
    +
    + +
    +
    +

    get_inference_program

    +
    +
    +paddle.v2.fluid.io.get_inference_program(target_vars, main_program=None)
    +
    +
    @@ -269,7 +405,7 @@ Next - Previous + Previous
    diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html index 7c937ccee1..46fde35a24 100644 --- a/develop/doc_cn/api/v2/fluid/layers.html +++ b/develop/doc_cn/api/v2/fluid/layers.html @@ -8,7 +8,7 @@ - Layers — PaddlePaddle 文档 + layers — PaddlePaddle 文档 @@ -34,7 +34,7 @@ - + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • Layers
    • +
  • layers
    • @@ -236,127 +236,79 @@
    -

    Layers

    -
    -

    fc

    +

    layers

    +
    +

    control_flow

    +
    +

    split_lod_tensor

    -paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None)
    -

    Fully Connected Layer

    -

    The fully connected layer can take multiple tensors as its inputs. It -creates a variable (one for each input tensor) called weights for each -input tensor, which represents a fully connected weight matrix from -each input unit to each output unit. The fully connected layer -multiplies each input tensor with its coresponding weight to produce -an output Tensor. If multiple input tensors are given, the results of -multiple multiplications will be sumed up. If bias_attr is not None, -a biases variable will be created and added to the output. Finally, -if activation is not None, it will be applied to the output as well.

    -

    This process can be formulated as follows:

    -
    -\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]
    -

    In the above equation:

    -
      -
    • \(N\): Number of the input.
      • -
    • \(X_i\): The input tensor.
      • -
    • \(W\): The weights created by this layer.
      • -
    • \(b\): The bias parameter created by this layer (if needed).
      • -
    • \(Act\): The activation funtion.
      • -
    • \(Out\): The output tensor.
      • -
    +paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0) +

    split_lod_tensor

    +

    This function takes in an input that contains the complete lod information, +and takes in a mask which is used to mask certain parts of the input. +The output is the true branch and the false branch with the mask applied to +the input at a certain level in the tensor.

    - - - -
    参数:
      -
    • input (Variable|list) – The input tensor(s) to the fully connected layer.
      • -
    • size (int) – The number of output units in the fully connected layer.
      • -
    • num_flatten_dims (int) – The fc layer can accept an input tensor with more -than two dimensions. If this happens, the -multidimensional tensor will first be flattened -into a 2-dimensional matrix. The parameter -num_flatten_dims determines how the input tensor -is flattened: the first num_flatten_dims -(inclusive, index starts from 1) dimensions will -be flatten to form the first dimension of the -final matrix (height of the matrix), and the rest -rank(X) - num_flatten_dims dimensions are -flattened to form the second dimension of the -final matrix (width of the matrix). For example, -suppose X is a 6-dimensional tensor with a shape -[2, 3, 4, 5, 6], and num_flatten_dims = 3. Then, -the flattened matrix will have a shape -[2 x 3 x 4, 5 x 6] = [24, 30]. By default, -num_flatten_dims is set to 1.
      • -
    • param_attr (ParamAttr|list) – The parameter attribute for learnable -parameters/weights of the fully connected -layer.
      • -
    • param_initializer (ParamAttr|list) – The initializer used for the -weight/parameter. If set None, -XavierInitializer() will be used.
      • -
    • bias_attr (ParamAttr|list) – The parameter attribute for the bias parameter -for this layer. If set None, no bias will be -added to the output units.
      • -
    • bias_initializer (ParamAttr|list) – The initializer used for the bias. -If set None, then ConstantInitializer() -will be used.
      • -
    • act (str) – Activation to be applied to the output of the fully connected -layer.
      • -
    • name (str) – Name/alias of the fully connected layer.
      • +
    • input (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
      • +
    • mask (list) – A bool column vector which masks the input.
      • +
    • level (int) – The specific lod level to rank.
    返回:

    The output tensor variable.

    -
    返回类型:

    Variable

    +
    返回:

    The true branch of tensor as per the mask applied to input. +Variable: The false branch of tensor as per the mask applied to input.
    Raises:

    ValueError – If rank of the input tensor is less than 2.

    +
    返回类型:

    Variable

    Examples

    -
    data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
-fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+
    x = layers.data(name='x', shape=[1])
+x.persistable = True
+
+y = layers.data(name='y', shape=[1])
+y.persistable = True
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
    -
    -

    embedding

    +
    +

    merge_lod_tensor

    -paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, padding_idx=None, param_attr=None, dtype='float32')
    -

    Embedding Layer

    -

    This layer is used to lookup embeddings of IDs, provided by input, in -a lookup table. The result of this lookup is the embedding of each ID in the -input.

    -

    All the input variables are passed in as local variables to the LayerHelper -constructor.

    +paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0) +

    merge_lod_tensor

    +

    This function takes in an input \(x\), the True branch, the False +branch and a binary \(mask\). Using this information, this function +merges the True and False branches of the tensor into a single Output +at a certain lod level indiacted by \(level\).

    - + + + + +
    参数:
      -
    • input (Variable) – The tensor variable containing the IDs.
      • -
    • size (tuple|list) – The shape of the look up table parameter. It should -have two elements which indicate the size of the dictionary of -embeddings and the size of each embedding vector respectively.
      • -
    • is_sparse (bool) – The flag indicating whether to use sparse update.
      • -
    • padding_idx (int|long|None) – If None, it makes no effect to lookup. -Otherwise the given padding_idx indicates padding the output -with zeros whenever lookup encounters it in input. If -\(padding_idx < 0\), the padding_idx to use in lookup is -\(size[0] + dim\).
      • -
    • param_attr (ParamAttr) – Parameters for this layer
      • -
    • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
      • +
    • in_true (tuple|list|None) – The True branch to be merged.
      • +
    • in_false (tuple|list|None) – The False branch to be merged.
      • +
    • x (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
      • +
    • mask (list) – A bool column vector which masks the input.
      • +
    • level (int) – The specific lod level to rank.
    返回:

    The tensor variable storing the embeddings of the supplied inputs.

    +
    返回:

    The merged output tensor.
    返回类型:

    Variable

    @@ -365,344 +317,209 @@ with zeros whenever lookup encounters it in Examples

    -
    dict_size = len(dataset.ids)
-data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
-fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
+
    x = layers.data(
+            name='x', shape=[1], dtype='float32', stop_gradient=False)
+y = layers.data(
+      name='y', shape=[1], dtype='bool', stop_gradient=False)
+
+level = 0
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
+out = layers.merge_lod_tensor(
+      in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
    -
    -

    dynamic_lstm

    -
    +
    +

    BlockGuard

    +
    -paddle.v2.fluid.layers.dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32', name=None)
    -

    Dynamic LSTM Layer

    -

    The defalut implementation is diagonal/peephole connection -(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:

    -
    -\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\end{aligned}\end{align} \]
    -

    where the \(W\) terms denote weight matrices (e.g. \(W_{xi}\) is -the matrix of weights from the input gate to the input), \(W_{ic}, W_{fc}, W_{oc}\) are diagonal weight matrices for peephole connections. In -our implementation, we use vectors to reprenset these diagonal weight -matrices. The \(b\) terms denote bias vectors (\(b_i\) is the input -gate bias vector), \(\sigma\) is the non-linear activations, such as -logistic sigmoid function, and \(i, f, o\) and \(c\) are the input -gate, forget gate, output gate, and cell activation vectors, respectively, -all of which have the same size as the cell output activation vector \(h\).

    -

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) -and \(act_h\) are the cell input and cell output activation functions -and tanh is usually used for them. \(\tilde{c_t}\) is also called -candidate hidden state, which is computed based on the current input and -the previous hidden state.

    -

    Set use_peepholes to False to disable peephole connection. The formula -is omitted here, please refer to the paper -http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    -

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) -operations on the input \(x_{t}\) are NOT included in this operator. -Users can choose to use fully-connect layer before LSTM layer.

    +class paddle.v2.fluid.layers.BlockGuard(main_program) +

    BlockGuard class.

    +

    BlockGuard class is used to create a sub-block in a program by +using the Python with keyword.

    +
    + +
    +
    +

    BlockGuardWithCompletion

    +
    +
    +class paddle.v2.fluid.layers.BlockGuardWithCompletion(rnn)
    +

    BlockGuardWithCompletion class.

    +

    BlockGuardWithCompletion class is used to create an op with a block in a program.

    +
    + +
    + -
    -

    dynamic_lstmp

    +
    +

    WhileGuard

    +
    +
    +class paddle.v2.fluid.layers.WhileGuard(while_op)
    +
    + +
    +
    +

    While

    +
    +
    +class paddle.v2.fluid.layers.While(cond, name=None)
    +
    + +
    +
    +

    lod_rank_table

    -paddle.v2.fluid.layers.dynamic_lstmp(input, size, proj_size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', proj_activation='tanh', dtype='float32', name=None)
    -

    Dynamic LSTMP Layer

    -

    LSTMP (LSTM with recurrent projection) layer has a separate projection -layer after the LSTM layer, projecting the original hidden state to a -lower-dimensional one, which is proposed to reduce the number of total -parameters and furthermore computational complexity for the LSTM, -espeacially for the case that the size of output units is relative -large (https://research.google.com/pubs/archive/43905.pdf).

    -

    The formula is as follows:

    -
    -\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\\r_t & = \overline{act_h}(W_{rh}h_t)\end{aligned}\end{align} \]
    -

    In the above formula:

    -
      -
    • \(W\): Denotes weight matrices (e.g. \(W_{xi}\) is the matrix of weights from the input gate to the input).
      • -
    • \(W_{ic}\), \(W_{fc}\), \(W_{oc}\): Diagonal weight matrices for peephole connections. In our implementation, we use vectors to reprenset these diagonal weight matrices.
      • -
    • \(b\): Denotes bias vectors (e.g. \(b_i\) is the input gate bias vector).
      • -
    • \(\sigma\): The activation, such as logistic sigmoid function.
      • -
    • \(i, f, o\) and \(c\): The input gate, forget gate, output gate, and cell activation vectors, respectively, all of which have the same size as the cell output activation vector \(h\).
      • -
    • \(h\): The hidden state.
      • -
    • \(r\): The recurrent projection of the hidden state.
      • -
    • \(\tilde{c_t}\): The candidate hidden state, whose computation is based on the current input and previous hidden state.
      • -
    • \(\odot\): The element-wise product of the vectors.
      • -
    • \(act_g\) and \(act_h\): The cell input and cell output activation functions and tanh is usually used for them.
      • -
    • \(\overline{act_h}\): The activation function for the projection output, usually using identity or same as \(act_h\).
      • -
    -

    Set use_peepholes to False to disable peephole connection. The formula -is omitted here, please refer to the paper -http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    -

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) -operations on the input \(x_{t}\) are NOT included in this operator. -Users can choose to use fully-connected layer before LSTMP layer.

    +paddle.v2.fluid.layers.lod_rank_table(x, level=0) +

    LoD Rank Table Operator. Given an input variable x and a level number +of LoD, this layer creates a LodRankTable object. A LoDRankTable object +contains a list of bi-element tuples. Each tuple consists of an index and +a length, both of which are int type. Refering to specified level of LoD, +the index is the sequence index number and the length representes the +sequence length. Please note that the list is ranked in descending order by +the length. The following is an example:

    +
    +
    x is a LoDTensor:
+    x.lod = [[0,                2, 3],
+             [0,             5, 6, 7]]
+    x.data = [a, b, c, d, e, f, g]
+
+1. set level to 0:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=0)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 2), (1, 1)]
+
+2. set level to 1:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=1)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]
+
    +
    +
    - -
    参数:
      -
    • input (Variable) – The input of dynamic_lstmp layer, which supports -variable-time length input sequence. The underlying -tensor in this Variable is a matrix with shape -(T X 4D), where T is the total time steps in this -mini-batch, D is the hidden size.
      • -
    • size (int) – 4 * hidden size.
      • -
    • proj_size (int) – The size of projection output.
      • -
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable -hidden-hidden weight and projection weight.

      -
        -
      • Hidden-hidden weight = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}.
        • -
      • The shape of hidden-hidden weight is (P x 4D), -where P is the projection size and D the hidden -size.
        • -
      • Projection weight = {\(W_{rh}\)}.
        • -
      • The shape of projection weight is (D x P).
        • -
      -
      • -
    • bias_attr (ParamAttr|None) –

      The bias attribute for the learnable bias -weights, which contains two parts, input-hidden -bias weights and peephole connections weights if -setting use_peepholes to True.

      -
        -
      1. use_peepholes = False
        2. -
      -
      -
        -
      • Biases = {\(b_c, b_i, b_f, b_o\)}.
        • -
      • The shape is (1 x 4D).
        • -
      -
      -
        -
      1. use_peepholes = True
        2. -
      -
      -
        -
      • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
        • -
      • The shape is (1 x 7D).
        • -
      -
      -
      • -
    • use_peepholes (bool) – Whether to enable diagonal/peephole connections, -default True.
      • -
    • is_reverse (bool) – Whether to compute reversed LSTM, default False.
      • -
    • gate_activation (str) – The activation for input gate, forget gate and -output gate. Choices = [“sigmoid”, “tanh”, “relu”, -“identity”], default “sigmoid”.
      • -
    • cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, -“tanh”, “relu”, “identity”], default “tanh”.
      • -
    • candidate_activation (str) – The activation for candidate hidden state. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], -default “tanh”.
      • -
    • proj_activation (str) – The activation for projection output. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], -default “tanh”.
      • -
    • dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x (Variable) – Input variable, a LoDTensor based which to create the lod +rank table.
      • +
    • level (int) – Specify the LoD level, on which to create the lod rank +table.
    返回:

    The projection of hidden state, and cell state of LSTMP. The shape of projection is (T x P), for the cell state which is (T x D), and both LoD is the same with the input.

    +
    返回:

    The created LoDRankTable object.
    返回类型:

    tuple

    +
    返回类型:

    Variable

    Examples

    -
    hidden_dim, proj_dim = 512, 256
-fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
-                         act=None, bias_attr=None)
-proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
-                                         size=hidden_dim * 4,
-                                         proj_size=proj_dim,
-                                         use_peepholes=False,
-                                         is_reverse=True,
-                                         cell_activation="tanh",
-                                         proj_activation="tanh")
+
    x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+out = layers.lod_rank_table(x=x, level=0)
    -
    -

    dynamic_gru

    +
    +

    max_sequence_len

    -paddle.v2.fluid.layers.dynamic_gru(input, size, param_attr=None, bias_attr=None, is_reverse=False, gate_activation='sigmoid', candidate_activation='tanh', h_0=None)
    -

    Dynamic GRU Layer

    -

    Refer to Empirical Evaluation of Gated Recurrent Neural Networks on -Sequence Modeling

    -

    The formula is as follows:

    -
    -\[ \begin{align}\begin{aligned}u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)\\r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)\\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)\\h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \tilde{h_t}\end{aligned}\end{align} \]
    -

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) -is the update gate and reset gate activation function and \(sigmoid\) -is usually used for it. \(act_c\) is the activation function for -candidate hidden state and \(tanh\) is usually used for it.

    -

    Note that these \(W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}\) operations on -the input \(x_{t}\) are NOT included in this operator. Users can choose -to use fully-connect layer before GRU layer.

    +paddle.v2.fluid.layers.max_sequence_len(rank_table) +

    Max Sequence Len Operator. Given a LoDRankTable object, this layer +returns the max length of a batch of sequences. In fact, a LoDRankTable +object contains a list of tuples(<sequence index, sequence length>) and +the list is already sorted by sequence length in descending order, so the +operator just returns the sequence length of the first tuple element.

    - + - + - +
    参数:
      -
    • input (Variable) – The input of dynamic_gru layer, which supports -variable-time length input sequence. The underlying tensor in this -Variable is a matrix with shape \((T \times 3D)\), where -\(T\) is the total time steps in this mini-batch, \(D\) -is the hidden size.
      • -
    • size (int) – The dimension of the gru cell.
      • -
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable -hidden-hidden weight matrix. Note:

      -
        -
      • The shape of the weight matrix is \((T \times 3D)\), where -\(D\) is the hidden size.
        • -
      • All elements in the weight matrix can be divided into two parts. -The first part are weights of the update gate and reset gate with -shape \((D \times 2D)\), and the second part are weights for -candidate hidden state with shape \((D \times D)\).
        • -
      -
      • -
    • bias_attr (ParamAttr) – The parameter attribute for learnable the -hidden-hidden bias.
      • -
    • is_reverse (bool) – Whether to compute reversed GRU, default -False.
      • -
    • gate_activation (str) – The activation for update gate and reset gate. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “sigmoid”.
      • -
    • activation (str) – The activation for candidate hidden state. -Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “tanh”.
      • -
    -
    参数:rank_table (Variable) – Input variable which is a LoDRankTable object.
    返回:

    The hidden state of GRU. The shape is (T times D), and lod is the same with the input.

    -
    返回:The max length of sequence.
    返回类型:

    Variable

    -
    返回类型:Variable

    Examples

    -
    hidden_dim = 512
-x = fluid.layers.fc(input=data, size=hidden_dim * 3)
-hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
+
    x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+rank_table = layers.lod_rank_table(x=x, level=0)
+max_seq_len = layers.max_sequence_len(rank_table)
    -
    -

    data

    +
    +

    topk

    -paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, stop_gradient=True)
    -

    Data Layer

    -

    This function takes in the input and based on whether data has -to be returned back as a minibatch, it creates the global variable by using -the helper functions. The global variables can be accessed by all the -following operators in the graph.

    -

    All the input variables of this function are passed in as local variables -to the LayerHelper constructor.

    +paddle.v2.fluid.layers.topk(input, k) +

    topk

    +

    This function performs the operation that selects the k entries in the input +vector and outputs their values and indices as vectors. Thus topk_out[j] is +the j-th largest entry in input, and its index is topk_indices[j]

    -
    参数:
      -
    • name (str) – The name/alias of the function
      • -
    • shape (list) – Tuple declaring the shape.
      • -
    • append_batch_size (bool) – Whether or not to append the data as a batch.
      • -
    • dtype (int|float) – The type of data : float32, float_16, int etc
      • -
    • type (VarType) – The output type. By default it is LOD_TENSOR.
      • -
    • lod_level (int) – The LoD Level. 0 means the input data is not a sequence.
      • -
    • main_program (Program) – Name of the main program that calls this
      • -
    • startup_program (Program) – Name of the startup program
      • -
    • stop_gradient (bool) – A boolean that mentions whether gradient should flow.
      • +
    • input (Variable|list) – The input tensor that has all the data.
      • +
    • k (int) – The number of top elements that the function will pick.
    返回:

    The global variable that gives access to the data.

    +
    返回:

    +
    The variable of type array that contains the k largest entries
    +

    from input.

    +
    +
    Variable: The variable of type array that contains the indices of k
    +

    largest entries from input.

    +
    +
    +
    返回类型:

    Variable

    @@ -711,399 +528,388 @@ to the LayerHelper constructor.

    Examples

    -
    data = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
    x = fluid.layers.data(name='x', shape=[10])
+k = 5
+array = fluid.layers.topk(x, k)
    -
    -

    mean

    +
    +

    lod_tensor_to_array

    -paddle.v2.fluid.layers.mean(**kwargs)
    -

    Mean Operator.

    -

    Out is a scalar which is the mean of all elements in X.

    +paddle.v2.fluid.layers.lod_tensor_to_array(x, table) +

    Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.

    - - - + - -
    参数:x – The input of mean op -Duplicable: False Optional: False
    返回:The output of mean op
    参数:
      +
    • x (Variable|list) – The LOD tensor to be converted to a LOD tensor array.
      • +
    • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
      • +
    +
    -
    +
    返回:

    +
    The variable of type array that has been converted from a
    +

    tensor.

    +
    +
    +

    +
    返回类型:

    Variable

    +
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+
    +
    +
    -
    -

    mul

    +
    +

    array_to_lod_tensor

    -paddle.v2.fluid.layers.mul(**kwargs)
    -

    Mul Operator.

    -

    This operator is used to perform matrix multiplication for input $X$ and $Y$.

    -

    The equation is:

    -

    $$Out = X * Y$$

    -

    Both the input $X$ and $Y$ can carry the LoD (Level of Details) information, -or not. But the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.array_to_lod_tensor(x, table) +

    Convert a LoD_Tensor_Aarry to an LoDTensor.

    - + +
    参数:
      -
    • x – (Tensor), The first input tensor of mul op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of mul op. -Duplicable: False Optional: False
      • -
    • x_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two -dimensions as its inputs. If the input $X$ is a tensor with more -than two dimensions, $X$ will be flattened into a two-dimensional -matrix first. The flattening rule is: the first num_col_dims -will be flattened to form the first dimension of the final matrix -(the height of the matrix), and the rest rank(X) - num_col_dims -dimensions are flattened to form the second dimension of the final -matrix (the width of the matrix). As a result, height of the -flattened matrix is equal to the product of $X$’s first -x_num_col_dims dimensions’ sizes, and width of the flattened -matrix is equal to the product of $X$’s last rank(x) - num_col_dims -dimensions’ size. For example, suppose $X$ is a 6-dimensional -tensor with the shape [2, 3, 4, 5, 6], and x_num_col_dims = 3. -Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = -[24, 30].
      • -
    • y_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two, -dimensions as its inputs. If the input $Y$ is a tensor with more -than two dimensions, $Y$ will be flattened into a two-dimensional -matrix first. The attribute y_num_col_dims determines how $Y$ is -flattened. See comments of x_num_col_dims for more details.
      • +
    • x (Variable|list) – The lod tensor array to be converted to a tensor.
      • +
    • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
    返回:

    (Tensor), The output tensor of mul op.

    +
    返回:

    +
    The variable of type tensor that has been converted
    +

    from an array.

    +
    +
    +

    +
    返回类型:

    Variable
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
+
    +
    -
    -

    elementwise_add

    +
    +

    increment

    -paddle.v2.fluid.layers.elementwise_add(**kwargs)
    -

    Limited Elementwise Add Operator.

    -

    The equation is:

    -

    $$Out = X + Y$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-
    -
    -
    -
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True) +

    This function performs an operation that increments each value in the +input \(x\) by an amount: \(value\) as mentioned in the input +parameter. This operation is performed in-place by default.

    - + +
    参数:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    • x (Variable|list) – The tensor that has the input values.
      • +
    • value (float) – The amount by which the values should be incremented.
      • +
    • in_place (bool) – If the increment should be performed in-place.
    返回:

    The output of elementwise op.

    +
    返回:

    +
    The tensor variable storing the transformation of
    +

    element-wise increment of each value in the input.

    +
    +
    +

    +
    返回类型:

    Variable
    +

    Examples

    +
    data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
+data = fluid.layers.increment(x=data, value=3.0, in_place=True)
+
    +
    -
    -

    elementwise_sub

    +
    +

    array_write

    -paddle.v2.fluid.layers.elementwise_sub(**kwargs)
    -

    Limited Elementwise Sub Operator.

    -

    The equation is:

    -

    $$Out = X - Y$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-
    -
    -
    -
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.array_write(x, i, array=None) +

    This function writes the given input variable to the specified position +indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the +output LOD_TENSOR_ARRAY is not given(None), a new one will be created and +returned.

    - + +
    参数:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    • x (Variable|list) – The input tensor from which the data will be read.
      • +
    • i (Variable|list) – The index of the output LOD_TENSOR_ARRAY, pointing to +the position to which the input tensor will be +written.
      • +
    • array (Variable|list) – The output LOD_TENSOR_ARRAY to which the input +tensor will be written. If this parameter is +NONE, a new LOD_TENSOR_ARRAY will be created and +returned.
    返回:

    The output of elementwise op.

    +
    返回:

    The output LOD_TENSOR_ARRAY where the input tensor is written.

    +
    返回类型:

    Variable
    +

    Examples

    -
    -

    elementwise_mul

    +
    +

    create_array

    -paddle.v2.fluid.layers.elementwise_mul(**kwargs)
    -

    Limited Elementwise Mul Operator.

    -

    The equation is:

    -

    $$Out = X odotY$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+paddle.v2.fluid.layers.create_array(dtype)
+
    This function creates an array of type \(LOD_TENSOR_ARRAY\) using the
+LayerHelper.
    
+
+++
+
+
+
+
+
+
+
+
    


    参数:dtype (int|float) – The data type of the elements in the array.
    返回:The tensor variable storing the elements of data type.
    返回类型:Variable
    
+
    Examples
    
+
    data = fluid.layers.create_array(dtype='float32')
 
    
 
    
-
    
-
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +
    + +
    +
    +

    less_than

    +
    +
    +paddle.v2.fluid.layers.less_than(x, y, cond=None, **ignored)
    +

    Less than

    +

    This layer returns the truth value of \(x < y\) elementwise.

    - + +
    参数:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    • x (Variable) – First operand of less_than
      • +
    • y (Variable) – Second operand of less_than
      • +
    • cond (Variable|None) – Optional output variable to store the result of less_than
    返回:

    The output of elementwise op.

    +
    返回:

    The tensor variable storing the output of less_than.

    +
    返回类型:

    Variable
    +

    Examples

    +
    less = fluid.layers.less_than(x=label, y=limit)
+
    +
    -
    -

    elementwise_div

    +
    +

    array_read

    -paddle.v2.fluid.layers.elementwise_div(**kwargs)
    -

    Limited Elementwise Div Operator.

    -

    The equation is:

    -

    $$Out = X / Y$$

    -

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be -smaller than or equal to the dimensions of $X$.

    -

    There are two cases for this operator: -1. The shape of $Y$ is same with $X$; -2. The shape of $Y$ is a subset of $X$.

    -

    For case 2: -$Y$ will be broadcasted to match the shape of $X$ and axis should be -set to index of the start dimension to broadcast $Y$ onto $X$.

    -
    -
    For example
    -
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-
    -
    -
    -
    -

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) -information. However, the output only shares the LoD information with input $X$.

    +paddle.v2.fluid.layers.array_read(array, i) +

    This function performs the operation to read the data in as an +LOD_TENSOR_ARRAY. +:param array: The input tensor that will be written to an array. +:type array: Variable|list +:param i: The subscript index in tensor array, that points the

    +
    +
    place where data will be written to.
    - + - +
    参数:
      -
    • x – (Tensor), The first input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • y – (Tensor), The second input tensor of elementwise op. -Duplicable: False Optional: False
      • -
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • -
    -
    返回:The tensor type variable that has the data written to it.
    返回:

    The output of elementwise op.

    -
    返回类型:Variable
    +

    Examples

    -
    -

    dropout

    +
    +

    shrink_memory

    -paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=None, **kwargs)
    -

    Computes dropout.

    -

    Drop or keep each element of x independently. Dropout is a regularization -technique for reducing overfitting by preventing neuron co-adaption during -training. The dropout operator randomly set (according to the given dropout -probability) the outputs of some units to zero, while others are remain -unchanged.

    +paddle.v2.fluid.layers.shrink_memory(x, i, table) +

    This function creates an operator to shrink_rnn_memory using the RankTable +as mentioned in the input parameter.

    +
    + +
    +
    +

    array_length

    +
    +
    +paddle.v2.fluid.layers.array_length(array)
    +

    This function performs the operation to find the length of the input +LOD_TENSOR_ARRAY.

    - + - + - +
    参数:
      -
    • x (variable) – The input tensor.
      • -
    • dropout_prob (float) – Probability of setting units to zero.
      • -
    • is_test (bool) – A flag indicating whether it is in test phrase or not.
      • -
    • seed (int) – A Python integer used to create random seeds. If this -parameter is set to None, a random seed is used. -NOTE: If an integer seed is given, always the same output -units will be dropped. DO NOT use a fixed seed in training.
      • -
    -
    参数:array (LOD_TENSOR_ARRAY) – The input array that will be used +to compute the length.
    返回:

    A tensor variable.

    -
    返回:The length of the input LoDTensorArray.
    返回类型:

    Variable

    -
    返回类型:Variable

    Examples

    -
    x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
-droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
-
    -
    -
    -

    reshape

    -
    +
    +

    IfElse

    +
    -paddle.v2.fluid.layers.reshape(**kwargs)
    -

    Reshape Operator.

    -

    Reshape Input(X) into the shape specified by Attr(shape).

    -

    An example: -Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]

    -

    and target shape = [1, 4], the reshape operator will transform -the tensor X into a 2-D tensor: [[1, 2, 3, 4]]

    -

    One dimension in the target shape can be set -1, representing that its -size is unknown. In this case, the real dimension will be infered from -the original shape of Input(X) and other dimensions in the target shape.

    - +class paddle.v2.fluid.layers.IfElse(cond, name=None) +
    + + +
    +

    DynamicRNN

    +
    +
    +class paddle.v2.fluid.layers.DynamicRNN(name=None)
    +
    + +
    +
    +

    ConditionalBlock

    +
    +
    +class paddle.v2.fluid.layers.ConditionalBlock(inputs, name=None)
    +
    + +
    +
    +

    StaticRNN

    +
    +
    +class paddle.v2.fluid.layers.StaticRNN(name=None)
    +

    StaticRNN class.

    +

    StaticRNN class is used to create a StaticRNN. The RNN will have its +own parameters like inputs, outputs, memories, status and length.

    +
    +
    +memory(init=None, shape=None, batch_ref=None, init_value=0.0, init_batch_dim_idx=0, ref_batch_dim_idx=1)
    +
    - - -
    参数:
      -
    • x – The input tensor of reshape operator. -Duplicable: False Optional: False
      • -
    • shape (INTS) – (vector<int>) Target shape of reshape operator.
      • +
    参数:
      +
    • init – boot memory, if not set, a shape, batch_ref must be provided
      • +
    • shape – shape of the boot memory
      • +
    • batch_ref – batch size reference variable
      • +
    • init_value – the init value of boot memory
      • +
    • init_batch_dim_idx – the index of batch size in init’s dimension
      • +
    • ref_batch_dim_idx – the index of batch size in batch_ref’s dimension
    返回:

    The output tensor of reshape operator.

    -
    -
    -
    -

    sigmoid

    -
    -
    -paddle.v2.fluid.layers.sigmoid(**kwargs)
    -

    Sigmoid Activation Operator

    -

    $$out = frac{1}{1 + e^{-x}}$$

    - --- - - - - - -
    参数:x – Input of Sigmoid operator -Duplicable: False Optional: False
    返回:Output of Sigmoid operator
    -
    -

    scale

    +
    +

    reorder_lod_tensor_by_rank

    -paddle.v2.fluid.layers.scale(**kwargs)
    -

    Scale operator

    -

    $$Out = scale*X$$

    +paddle.v2.fluid.layers.reorder_lod_tensor_by_rank(x, rank_table) +

    ReorderLoDTensorByRankTable operator.

    +

    Input(X) is a batch of sequences. Input(RankTable) stores new orders of the +input sequence batch. The reorder_lod_tensor_by_rank operator reorders the +Input(X) according to the information provided by Input(RankTable).

    +

    For example:

    +

    If the indices stored in the Input(RankTable) are [3, 0, 2, 1], the +Input(X) will be reordered that the fourth sequence in Input(X) will become the +first one, and then followed by the original first, third, and the second one.

    +

    This is: +X = [Seq0, Seq1, Seq2, Seq3]. The indices in RankTable are [3, 0, 2, 1]. +Out = [Seq3, Seq0, Seq2, Seq1] with a new LoD information.

    +

    If the LoD information of Input(X) is empty, this means Input(X) is not sequence +data. This is also identical to a batch of sequences where each sequence has a +fixed length 1. In this case, the reorder_lod_tensor_by_rank operator reorders +each slice of Input(X) along the first axis according to Input(RankTable).

    +

    This is: +X = [Slice0, Slice1, Slice2, Slice3] and its LoD information is empty. The +indices in RankTable are [3, 0, 2, 1]. +Out = [Slice3, Slice0, Slice2, Slice1] with no LoD information is appended.

    +

    NOTE: This operator sorts Input(X) according to a given LoDRankTable which does +not need to be calculated according to Input(X). It can be calculated according +to another different sequence, and then this operator sorts Input(X) according +to the given LoDRankTable.

    - @@ -1111,26 +917,47 @@ Duplicable: False Optional: False -
    -

    transpose

    +
    +

    ParallelDo

    +
    +
    +class paddle.v2.fluid.layers.ParallelDo(places, name=None)
    +

    ParallelDo class.

    +

    ParallelDo class is used to create a ParallelDo.

    +
    + +
    +
    +

    Print

    -paddle.v2.fluid.layers.transpose(x, perm, name=None)
    -

    transpose Layer

    -

    Permute the dimensions of input according to perm.

    -

    The i-th dimension of the returned tensor will correspond to the -perm[i]-th dimension of input.

    +paddle.v2.fluid.layers.Print(input, first_n=-1, message=None, summarize=-1, print_tensor_name=True, print_tensor_type=True, print_tensor_shape=True, print_tensor_lod=True, print_phase='both') +

    Print operator

    +

    This creates a print op that will print when a tensor is accessed.

    +

    Wraps the tensor passed in so that whenever that a tensor is accessed, +the message message is printed, along with the current value of the +tensor t.

    参数:
      -
    • x – (Tensor) Input tensor of scale operator. +
    • x – (LoDTensor), the input lod tensor to be reordered according to Input(RankTable). +Duplicable: False Optional: False
      • +
    • rank_table – (LoDRankTable), the rank table according to which Input(X) is reordered. Duplicable: False Optional: False
      • -
    • scale (FLOAT) – (float, default 1.0)The scaling factor of the scale operator.
    返回:

    (Tensor) Output tensor of scale operator.

    +
    返回:

    (LoDTensor), the reordered lod tensor.
    -
    参数:
      -
    • input (Variable) – (Tensor), A Tensor.
      • -
    • perm (list) – A permutation of the dimensions of input.
      • +
    • input (Variable) – A Tensor to print.
      • +
    • summarize (int) – Print this number of elements in the tensor, will print +all if left is negative.
      • +
    • message (str) – A string message to print as a prefix.
      • +
    • first_n (int) – Only log first_n number of times.
      • +
    • print_tensor_name (bool) – Print the tensor name.
      • +
    • print_tensor_type (bool) – Print the tensor type.
      • +
    • print_tensor_shape (bool) – Print the tensor shape.
      • +
    • print_tensor_lod (bool) – Print the tensor lod.
      • +
    • print_phase (bool) – Which phase to displace, including ‘forward’, +‘backward’ and ‘both’. If set to ‘backward’ or ‘both’, will +print the gradients of input tensor.
    返回:

    A transposed Tensor.

    +
    返回:

    Output tensor, same data with input tensor.
    返回类型:

    Variable

    @@ -1139,210 +966,260 @@ perm[i]-th dimension of input.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
-x_transposed = layers.transpose(x, perm=[1, 0, 2])
+
    
 
    
 
    
+
    value = some_layer(...)
+Print(value, summarize=10,
    
+
    
+
    message=”The content of some_layer: ”)
    -
    -

    sigmoid_cross_entropy_with_logits

    -
    -
    -

    cast

    -
    -
    -paddle.v2.fluid.layers.cast(x, dtype)
    -

    This function takes in the input with input_dtype -and casts it to the output_dtype as the output.

    -
    -
    -
    -

    concat

    +
    +

    device

    +
    +

    get_places

    -paddle.v2.fluid.layers.concat(input, axis=0)
    -

    Concat

    -

    This function concatenates the input along the axis mentioned -and returns that as the output.

    +paddle.v2.fluid.layers.get_places(device_count=None, device_type=None) +

    Returns a list of places based on flags. The list will be used for parallel +execution.

    - - -
    参数:
      -
    • input (list) – List of tensors to be concatenated
      • -
    • axis (int) – Integer axis along which the tensors will be concatenated
      • +
    • device_count (INT) – device count
      • +
    • device_type (STRING) – device type
    返回:

    Output variable of the concatenation

    -
    返回类型:

    Variable

    +
    返回:

    vector of Place
    -

    Examples

    -
    -

    sums

    +
    +
    +

    io

    +
    +

    data

    -paddle.v2.fluid.layers.sums(input, out=None)
    -

    This function performs the sum operation on the input and returns the -result as the output.

    +paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, stop_gradient=True) +

    Data Layer

    +

    This function takes in the input and based on whether data has +to be returned back as a minibatch, it creates the global variable by using +the helper functions. The global variables can be accessed by all the +following operators in the graph.

    +

    All the input variables of this function are passed in as local variables +to the LayerHelper constructor.

    - + - - +
    参数:input (Variable|list) – The input tensor that has the elements -that need to be summed up.
    参数:
      +
    • name (str) – The name/alias of the function
      • +
    • shape (list) – Tuple declaring the shape.
      • +
    • append_batch_size (bool) – Whether or not to append the data as a batch.
      • +
    • dtype (int|float) – The type of data : float32, float_16, int etc
      • +
    • type (VarType) – The output type. By default it is LOD_TENSOR.
      • +
    • lod_level (int) – The LoD Level. 0 means the input data is not a sequence.
      • +
    • main_program (Program) – Name of the main program that calls this
      • +
    • startup_program (Program) – Name of the startup program
      • +
    • stop_gradient (bool) – A boolean that mentions whether gradient should flow.
      • +
    +
    返回:
    -
    The tensor type variable that has the sum of input
    -
    written to it.
    -
    +
    返回:

    The global variable that gives access to the data.
    返回类型:Variable
    返回类型:

    Variable

    +

    Examples

    +
    data = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
    +
    -
    -

    linear_chain_crf

    -
    +
    +

    BlockGuardServ

    +
    -paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)
    -
    +class paddle.v2.fluid.layers.BlockGuardServ(server) +

    BlockGuardServ class.

    +

    BlockGuardServ class is used to create an op with a block in a program.

    +
    -
    -

    assign

    +
    +

    ListenAndServ

    +
    +
    +class paddle.v2.fluid.layers.ListenAndServ(endpoint, fan_in=1, optimizer_mode=True)
    +

    ListenAndServ class.

    +

    ListenAndServ class is used to wrap listen_and_serv op to create a server +which can receive variables from clients and run a block.

    +
    + +
    +
    +

    Send

    -paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, padding_idx=None, param_attr=None, dtype='float32')
    -

    Embedding Layer

    -

    This layer is used to lookup embeddings of IDs, provided by input, in -a lookup table. The result of this lookup is the embedding of each ID in the -input.

    -

    All the input variables are passed in as local variables to the LayerHelper -constructor.

    +paddle.v2.fluid.layers.Send(endpoints, send_vars, get_vars) +

    Send layer

    - - - - -
    参数:
      -
    • input (Variable) – The tensor variable containing the IDs.
      • -
    • size (tuple|list) – The shape of the look up table parameter. It should -have two elements which indicate the size of the dictionary of -embeddings and the size of each embedding vector respectively.
      • -
    • is_sparse (bool) – The flag indicating whether to use sparse update.
      • -
    • padding_idx (int|long|None) – If None, it makes no effect to lookup. -Otherwise the given padding_idx indicates padding the output -with zeros whenever lookup encounters it in input. If -\(padding_idx < 0\), the padding_idx to use in lookup is -\(size[0] + dim\).
      • -
    • param_attr (ParamAttr) – Parameters for this layer
      • -
    • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
      • +
    参数:
      +
    • endpoints – comma seperated IP:PORT pairs in the order +of send_vars to send
      • +
    • send_vars – vars to send
      • +
    • get_vars – vars to get from server after send completes.
    返回:

    The tensor variable storing the embeddings of the supplied inputs.

    -
    返回类型:

    Variable

    -
    -

    Examples

    -
    dict_size = len(dataset.ids)
-data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
-fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
-
    -
    +

    Send variables to the server side, and get vars from server +side when server have finished running server side program.

    -
    -

    split_lod_tensor

    +
    +
    +

    nn

    +
    +

    fc

    -paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0)
    -

    split_lod_tensor

    -

    This function takes in an input that contains the complete lod information, -and takes in a mask which is used to mask certain parts of the input. -The output is the true branch and the false branch with the mask applied to -the input at a certain level in the tensor.

    +paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None) +

    Fully Connected Layer

    +

    The fully connected layer can take multiple tensors as its inputs. It +creates a variable (one for each input tensor) called weights for each +input tensor, which represents a fully connected weight matrix from +each input unit to each output unit. The fully connected layer +multiplies each input tensor with its coresponding weight to produce +an output Tensor. If multiple input tensors are given, the results of +multiple multiplications will be sumed up. If bias_attr is not None, +a biases variable will be created and added to the output. Finally, +if activation is not None, it will be applied to the output as well.

    +

    This process can be formulated as follows:

    +
    +\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]
    +

    In the above equation:

    +
      +
    • \(N\): Number of the input.
      • +
    • \(X_i\): The input tensor.
      • +
    • \(W\): The weights created by this layer.
      • +
    • \(b\): The bias parameter created by this layer (if needed).
      • +
    • \(Act\): The activation funtion.
      • +
    • \(Out\): The output tensor.
      • +
    - - + +
    参数:
      -
    • input (tuple|list|None) – The input tensor that contains complete -lod information needed to construct the output.
      • -
    • mask (list) – A bool column vector which masks the input.
      • -
    • level (int) – The specific lod level to rank.
      • +
    • input (Variable|list) – The input tensor(s) to the fully connected layer.
      • +
    • size (int) – The number of output units in the fully connected layer.
      • +
    • num_flatten_dims (int) – The fc layer can accept an input tensor with more +than two dimensions. If this happens, the +multidimensional tensor will first be flattened +into a 2-dimensional matrix. The parameter +num_flatten_dims determines how the input tensor +is flattened: the first num_flatten_dims +(inclusive, index starts from 1) dimensions will +be flatten to form the first dimension of the +final matrix (height of the matrix), and the rest +rank(X) - num_flatten_dims dimensions are +flattened to form the second dimension of the +final matrix (width of the matrix). For example, +suppose X is a 6-dimensional tensor with a shape +[2, 3, 4, 5, 6], and num_flatten_dims = 3. Then, +the flattened matrix will have a shape +[2 x 3 x 4, 5 x 6] = [24, 30]. By default, +num_flatten_dims is set to 1.
      • +
    • param_attr (ParamAttr|list) – The parameter attribute for learnable +parameters/weights of the fully connected +layer.
      • +
    • param_initializer (ParamAttr|list) – The initializer used for the +weight/parameter. If set None, +XavierInitializer() will be used.
      • +
    • bias_attr (ParamAttr|list) – The parameter attribute for the bias parameter +for this layer. If set None, no bias will be +added to the output units.
      • +
    • bias_initializer (ParamAttr|list) – The initializer used for the bias. +If set None, then ConstantInitializer() +will be used.
      • +
    • act (str) – Activation to be applied to the output of the fully connected +layer.
      • +
    • name (str) – Name/alias of the fully connected layer.
    返回:

    The true branch of tensor as per the mask applied to input. -Variable: The false branch of tensor as per the mask applied to input.

    +
    返回:

    The output tensor variable.
    返回类型:

    Variable

    +
    返回类型:

    Variable

    +
    Raises:

    ValueError – If rank of the input tensor is less than 2.

    Examples

    -
    x = layers.data(name='x', shape=[1])
-x.persistable = True
-
-y = layers.data(name='y', shape=[1])
-y.persistable = True
-
-out_true, out_false = layers.split_lod_tensor(
-      input=x, mask=y, level=level)
+
    data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+fc = fluid.layers.fc(input=data, size=1000, act="tanh")
    -
    -

    merge_lod_tensor

    +
    +

    embedding

    -paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0)
    -

    merge_lod_tensor

    -

    This function takes in an input \(x\), the True branch, the False -branch and a binary \(mask\). Using this information, this function -merges the True and False branches of the tensor into a single Output -at a certain lod level indiacted by \(level\).

    +paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, padding_idx=None, param_attr=None, dtype='float32') +

    Embedding Layer

    +

    This layer is used to lookup embeddings of IDs, provided by input, in +a lookup table. The result of this lookup is the embedding of each ID in the +input.

    +

    All the input variables are passed in as local variables to the LayerHelper +constructor.

    -
    参数:
      -
    • in_true (tuple|list|None) – The True branch to be merged.
      • -
    • in_false (tuple|list|None) – The False branch to be merged.
      • -
    • x (tuple|list|None) – The input tensor that contains complete -lod information needed to construct the output.
      • -
    • mask (list) – A bool column vector which masks the input.
      • -
    • level (int) – The specific lod level to rank.
      • +
    • input (Variable) – The tensor variable containing the IDs.
      • +
    • size (tuple|list) – The shape of the look up table parameter. It should +have two elements which indicate the size of the dictionary of +embeddings and the size of each embedding vector respectively.
      • +
    • is_sparse (bool) – The flag indicating whether to use sparse update.
      • +
    • padding_idx (int|long|None) – If None, it makes no effect to lookup. +Otherwise the given padding_idx indicates padding the output +with zeros whenever lookup encounters it in input. If +\(padding_idx < 0\), the padding_idx to use in lookup is +\(size[0] + dim\).
      • +
    • param_attr (ParamAttr) – Parameters for this layer
      • +
    • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
    返回:

    The merged output tensor.

    +
    返回:

    The tensor variable storing the embeddings of the supplied inputs.
    返回类型:

    Variable

    @@ -1351,150 +1228,298 @@ lod information needed to construct the output.

    Examples

    -
    x = layers.data(
-            name='x', shape=[1], dtype='float32', stop_gradient=False)
-y = layers.data(
-      name='y', shape=[1], dtype='bool', stop_gradient=False)
-
-level = 0
-
-out_true, out_false = layers.split_lod_tensor(
-      input=x, mask=y, level=level)
-out = layers.merge_lod_tensor(
-      in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
+
    dict_size = len(dataset.ids)
+data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
+fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
    -
    -

    cos_sim

    -
    -
    -paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)
    -

    This function performs the cosine similarity between two tensors -X and Y and returns that as the output.

    -
    - -
    -
    -

    cross_entropy

    +
    +

    dynamic_lstm

    -paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)
    -

    Cross Entropy Layer

    -

    This layer computes the cross entropy between input and label. It -supports both standard cross-entropy and soft-label cross-entropy loss -computation.

    -
      -
    1. -
      One-hot cross-entropy:
      -

      soft_label = False, Label[i, 0] indicates the class index for sample i:

      -
      -\[Y[i] = -\log(X[i, Label[i]])\]
      -
      -
      -
      2. -
    2. -
      Soft-label cross-entropy:
      -

      soft_label = True, Label[i, j] indicates the soft label of class j -for sample i:

      -
      -\[Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}\]
      -
      -
      -

      Please make sure that in this case the summation of each row of label -equals one.

      -
      3. -
    3. -
      One-hot cross-entropy with vecterized label:
      -

      As a special case of 2), when each row of ‘label’ has only one -non-zero element which is equal to 1, soft-label cross-entropy degenerates -to a one-hot cross-entropy with one-hot label representation.

      -
      -
      -
      4. -
    +paddle.v2.fluid.layers.dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32', name=None) +

    Dynamic LSTM Layer

    +

    The defalut implementation is diagonal/peephole connection +(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:

    +
    +\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\end{aligned}\end{align} \]
    +

    where the \(W\) terms denote weight matrices (e.g. \(W_{xi}\) is +the matrix of weights from the input gate to the input), \(W_{ic}, W_{fc}, W_{oc}\) are diagonal weight matrices for peephole connections. In +our implementation, we use vectors to reprenset these diagonal weight +matrices. The \(b\) terms denote bias vectors (\(b_i\) is the input +gate bias vector), \(\sigma\) is the non-linear activations, such as +logistic sigmoid function, and \(i, f, o\) and \(c\) are the input +gate, forget gate, output gate, and cell activation vectors, respectively, +all of which have the same size as the cell output activation vector \(h\).

    +

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) +and \(act_h\) are the cell input and cell output activation functions +and tanh is usually used for them. \(\tilde{c_t}\) is also called +candidate hidden state, which is computed based on the current input and +the previous hidden state.

    +

    Set use_peepholes to False to disable peephole connection. The formula +is omitted here, please refer to the paper +http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    +

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) +operations on the input \(x_{t}\) are NOT included in this operator. +Users can choose to use fully-connect layer before LSTM layer.

    - - - - + + + +
    参数:
      -
    • input (Variable|list) – a 2-D tensor with shape [N x D], where N is the -batch size and D is the number of classes. This -input is a probability computed by the previous -operator, which is almost always the result of -a softmax operator.
      • -
    • label (Variable|list) – the ground truth which is a 2-D tensor. When -soft_label is set to False, label is a -tensor<int64> with shape [N x 1]. When -soft_label is set to True, label is a -tensor<float/double> with shape [N x D].
      • -
    • soft_label (bool, via **kwargs) – a flag indicating whether to -interpretate the given labels as soft -labels, default False.
      • +
    • input (Variable) – The input of dynamic_lstm layer, which supports +variable-time length input sequence. The underlying +tensor in this Variable is a matrix with shape +(T X 4D), where T is the total time steps in this +mini-batch, D is the hidden size.
      • +
    • size (int) – 4 * hidden size.
      • +
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable +hidden-hidden weights.

      +
        +
      • Weights = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}
        • +
      • The shape is (D x 4D), where D is the hidden +size.
      -
    返回:

    A 2-D tensor with shape [N x 1], the cross entropy loss.

    -
    Raises:

    ValueError – 1) the 1st dimension of input and label are not equal. -2) when soft_label == True, and the 2nd dimension of

    + +
  • bias_attr (ParamAttr|None) –

    The bias attribute for the learnable bias +weights, which contains two parts, input-hidden +bias weights and peephole connections weights if +setting use_peepholes to True.

    +
      +
    1. use_peepholes = False
      2. +
    -

    input and label are not equal.

    -
    -
      -
    1. when soft_label == False, and the 2nd dimension of -label is not 1.
      2. +
        +
      • Biases = {\(b_c, b_i, b_f, b_o\)}.
        • +
      • The shape is (1 x 4D).
        • +
      +
      +
        +
      1. use_peepholes = True
      +
      +
        +
      • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
        • +
      • The shape is (1 x 7D).
        • +
      +
      + +
    2. use_peepholes (bool) – Whether to enable diagonal/peephole connections, +default True.
      3. +
    3. is_reverse (bool) – Whether to compute reversed LSTM, default False.
      4. +
    4. gate_activation (str) – The activation for input gate, forget gate and +output gate. Choices = [“sigmoid”, “tanh”, “relu”, +“identity”], default “sigmoid”.
      5. +
    5. cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, +“tanh”, “relu”, “identity”], default “tanh”.
      6. +
    6. candidate_activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
      7. +
    7. dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
      8. +
    8. name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      9. + +
    • 返回:

    The hidden state, and cell state of LSTM. The shape of both is (T x D), and lod is the same with the input.

    +
    返回类型:

    tuple

    Examples

    -
    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
-cost = fluid.layers.cross_entropy(input=predict, label=label)
+
    hidden_dim = 512
+forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                               act=None, bias_attr=None)
+forward, _ = fluid.layers.dynamic_lstm(
+    input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
    -
    -

    square_error_cost

    +
    +

    dynamic_lstmp

    -paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)
    -

    Square error cost layer

    -

    This layer accepts input predictions and target label and returns the -squared error cost.

    -

    For predictions, \(X\), and target labels, \(Y\), the equation is:

    +paddle.v2.fluid.layers.dynamic_lstmp(input, size, proj_size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', proj_activation='tanh', dtype='float32', name=None) +

    Dynamic LSTMP Layer

    +

    LSTMP (LSTM with recurrent projection) layer has a separate projection +layer after the LSTM layer, projecting the original hidden state to a +lower-dimensional one, which is proposed to reduce the number of total +parameters and furthermore computational complexity for the LSTM, +espeacially for the case that the size of output units is relative +large (https://research.google.com/pubs/archive/43905.pdf).

    +

    The formula is as follows:

    -\[Out = (X - Y)^2\]
    -

    In the above equation:

    +\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\\r_t & = \overline{act_h}(W_{rh}h_t)\end{aligned}\end{align} \]
    +

    In the above formula:

    +
      +
    • \(W\): Denotes weight matrices (e.g. \(W_{xi}\) is the matrix of weights from the input gate to the input).
      • +
    • \(W_{ic}\), \(W_{fc}\), \(W_{oc}\): Diagonal weight matrices for peephole connections. In our implementation, we use vectors to reprenset these diagonal weight matrices.
      • +
    • \(b\): Denotes bias vectors (e.g. \(b_i\) is the input gate bias vector).
      • +
    • \(\sigma\): The activation, such as logistic sigmoid function.
      • +
    • \(i, f, o\) and \(c\): The input gate, forget gate, output gate, and cell activation vectors, respectively, all of which have the same size as the cell output activation vector \(h\).
      • +
    • \(h\): The hidden state.
      • +
    • \(r\): The recurrent projection of the hidden state.
      • +
    • \(\tilde{c_t}\): The candidate hidden state, whose computation is based on the current input and previous hidden state.
      • +
    • \(\odot\): The element-wise product of the vectors.
      • +
    • \(act_g\) and \(act_h\): The cell input and cell output activation functions and tanh is usually used for them.
      • +
    • \(\overline{act_h}\): The activation function for the projection output, usually using identity or same as \(act_h\).
      • +
    +

    Set use_peepholes to False to disable peephole connection. The formula +is omitted here, please refer to the paper +http://www.bioinf.jku.at/publications/older/2604.pdf for details.

    +

    Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) +operations on the input \(x_{t}\) are NOT included in this operator. +Users can choose to use fully-connected layer before LSTMP layer.

    + +++ + + + + + + + +
    参数:
      +
    • input (Variable) – The input of dynamic_lstmp layer, which supports +variable-time length input sequence. The underlying +tensor in this Variable is a matrix with shape +(T X 4D), where T is the total time steps in this +mini-batch, D is the hidden size.
      • +
    • size (int) – 4 * hidden size.
      • +
    • proj_size (int) – The size of projection output.
      • +
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable +hidden-hidden weight and projection weight.

      +
        +
      • Hidden-hidden weight = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}.
        • +
      • The shape of hidden-hidden weight is (P x 4D), +where P is the projection size and D the hidden +size.
        • +
      • Projection weight = {\(W_{rh}\)}.
        • +
      • The shape of projection weight is (D x P).
        • +
      +
      • +
    • bias_attr (ParamAttr|None) –

      The bias attribute for the learnable bias +weights, which contains two parts, input-hidden +bias weights and peephole connections weights if +setting use_peepholes to True.

      +
        +
      1. use_peepholes = False
        2. +
      -
        -
      • \(X\): Input predictions, a tensor.
        • -
      • \(Y\): Input labels, a tensor.
        • -
      • \(Out\): Output value, same shape with \(X\).
        • +
          +
        • Biases = {\(b_c, b_i, b_f, b_o\)}.
          • +
        • The shape is (1 x 4D).
          • +
        +
      +
        +
      1. use_peepholes = True
        2. +
      +
      +
        +
      • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
        • +
      • The shape is (1 x 7D).
      +
      • +
    • use_peepholes (bool) – Whether to enable diagonal/peephole connections, +default True.
      • +
    • is_reverse (bool) – Whether to compute reversed LSTM, default False.
      • +
    • gate_activation (str) – The activation for input gate, forget gate and +output gate. Choices = [“sigmoid”, “tanh”, “relu”, +“identity”], default “sigmoid”.
      • +
    • cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, +“tanh”, “relu”, “identity”], default “tanh”.
      • +
    • candidate_activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
      • +
    • proj_activation (str) – The activation for projection output. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
      • +
    • dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    返回:

    The projection of hidden state, and cell state of LSTMP. The shape of projection is (T x P), for the cell state which is (T x D), and both LoD is the same with the input.

    +
    返回类型:

    tuple

    +
    +

    Examples

    +
    hidden_dim, proj_dim = 512, 256
+fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                         act=None, bias_attr=None)
+proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
+                                         size=hidden_dim * 4,
+                                         proj_size=proj_dim,
+                                         use_peepholes=False,
+                                         is_reverse=True,
+                                         cell_activation="tanh",
+                                         proj_activation="tanh")
+
    +
    +
    + +
    +
    +

    dynamic_gru

    +
    +
    +paddle.v2.fluid.layers.dynamic_gru(input, size, param_attr=None, bias_attr=None, is_reverse=False, gate_activation='sigmoid', candidate_activation='tanh', h_0=None)
    +

    Dynamic GRU Layer

    +

    Refer to Empirical Evaluation of Gated Recurrent Neural Networks on +Sequence Modeling

    +

    The formula is as follows:

    +
    +\[ \begin{align}\begin{aligned}u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)\\r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)\\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)\\h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \tilde{h_t}\end{aligned}\end{align} \]
    +

    The \(\odot\) is the element-wise product of the vectors. \(act_g\) +is the update gate and reset gate activation function and \(sigmoid\) +is usually used for it. \(act_c\) is the activation function for +candidate hidden state and \(tanh\) is usually used for it.

    +

    Note that these \(W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}\) operations on +the input \(x_{t}\) are NOT included in this operator. Users can choose +to use fully-connect layer before GRU layer.

    -
    参数:
      -
    • input (Variable) – Input tensor, has predictions.
      • -
    • label (Variable) – Label tensor, has target labels.
      • +
    • input (Variable) – The input of dynamic_gru layer, which supports +variable-time length input sequence. The underlying tensor in this +Variable is a matrix with shape \((T \times 3D)\), where +\(T\) is the total time steps in this mini-batch, \(D\) +is the hidden size.
      • +
    • size (int) – The dimension of the gru cell.
      • +
    • param_attr (ParamAttr|None) –

      The parameter attribute for the learnable +hidden-hidden weight matrix. Note:

      +
        +
      • The shape of the weight matrix is \((T \times 3D)\), where +\(D\) is the hidden size.
        • +
      • All elements in the weight matrix can be divided into two parts. +The first part are weights of the update gate and reset gate with +shape \((D \times 2D)\), and the second part are weights for +candidate hidden state with shape \((D \times D)\).
        • +
      +
      • +
    • bias_attr (ParamAttr) – The parameter attribute for learnable the +hidden-hidden bias.
      • +
    • is_reverse (bool) – Whether to compute reversed GRU, default +False.
      • +
    • gate_activation (str) – The activation for update gate and reset gate. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “sigmoid”.
      • +
    • activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], default “tanh”.
    返回:

    -
    The tensor variable storing the element-wise squared error
    -

    difference of input and label.

    -
    -
    -

    +
    返回:

    The hidden state of GRU. The shape is (T times D), and lod is the same with the input.
    返回类型:

    Variable

    @@ -1503,199 +1528,884 @@ squared error cost.

    Examples

    -
    y = layers.data(name='y', shape=[1], dtype='float32')
-y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
-cost = layers.square_error_cost(input=y_predict, label=y)
+
    hidden_dim = 512
+x = fluid.layers.fc(input=data, size=hidden_dim * 3)
+hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
+
    
+
    
+
    + +
    +
    +

    gru_unit

    +
    +
    +paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')
    +

    GRU unit layer. The equation of a gru step is:

    +
    +
    +\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)\\h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})\end{aligned}\end{align} \]
    +
    +

    The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms +of the equation above, the \(z_t\) is split into 3 parts - +\(xu_t\), \(xr_t\) and \(xm_t\). This means that in order to +implement a full GRU unit operator for an input, a fully +connected layer has to be applied, such that \(z_t = W_{fc}x_t\).

    +

    The terms \(u_t\) and \(r_t\) represent the update and reset gates +of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is +an intermediate candidate hidden output, which is denoted by \(m_t\). +This layer has three outputs \(h_t\), \(dot(r_t, h_{t-1})\) +and concatenation of \(u_t\), \(r_t\) and \(m_t\).

    + +++ + + + + + + + +
    参数:
      +
    • input (Variable) – The fc transformed input value of current step.
      • +
    • hidden (Variable) – The hidden value of lstm unit from previous step.
      • +
    • size (integer) – The input dimension value.
      • +
    • weight (ParamAttr) – The weight parameters for gru unit. Default: None
      • +
    • bias (ParamAttr) – The bias parameters for gru unit. Default: None
      • +
    • activation (string) – The activation type for cell (actNode). +Default: ‘tanh’
      • +
    • gate_activation (string) – The activation type for gates (actGate). +Default: ‘sigmoid’
      • +
    +
    返回:

    The hidden value, reset-hidden value and gate values.

    +
    返回类型:

    tuple

    +
    +

    Examples

    +
    # assuming we have x_t_data and prev_hidden of size=10
+x_t = fluid.layers.fc(input=x_t_data, size=30)
+hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
+                                       hidden = prev_hidden)
+
    +
    +
    + +
    +
    +

    linear_chain_crf

    +
    +
    +paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)
    +
    + +
    +
    +

    crf_decoding

    +
    +
    +paddle.v2.fluid.layers.crf_decoding(input, param_attr, label=None)
    +
    + +
    +
    +

    cos_sim

    +
    +
    +paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)
    +

    This function performs the cosine similarity between two tensors +X and Y and returns that as the output.

    +
    + +
    +
    +

    cross_entropy

    +
    +
    +paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)
    +

    Cross Entropy Layer

    +

    This layer computes the cross entropy between input and label. It +supports both standard cross-entropy and soft-label cross-entropy loss +computation.

    +
      +
    1. +
      One-hot cross-entropy:
      +

      soft_label = False, Label[i, 0] indicates the class index for sample i:

      +
      +\[Y[i] = -\log(X[i, Label[i]])\]
      +
      +
      +
      2. +
    2. +
      Soft-label cross-entropy:
      +

      soft_label = True, Label[i, j] indicates the soft label of class j +for sample i:

      +
      +\[Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}\]
      +
      +
      +

      Please make sure that in this case the summation of each row of label +equals one.

      +
      3. +
    3. +
      One-hot cross-entropy with vecterized label:
      +

      As a special case of 2), when each row of ‘label’ has only one +non-zero element which is equal to 1, soft-label cross-entropy degenerates +to a one-hot cross-entropy with one-hot label representation.

      +
      +
      +
      4. +
    + +++ + + + + + + + +
    参数:
      +
    • input (Variable|list) – a 2-D tensor with shape [N x D], where N is the +batch size and D is the number of classes. This +input is a probability computed by the previous +operator, which is almost always the result of +a softmax operator.
      • +
    • label (Variable|list) – the ground truth which is a 2-D tensor. When +soft_label is set to False, label is a +tensor<int64> with shape [N x 1]. When +soft_label is set to True, label is a +tensor<float/double> with shape [N x D].
      • +
    • soft_label (bool, via **kwargs) – a flag indicating whether to +interpretate the given labels as soft +labels, default False.
      • +
    +
    返回:

    A 2-D tensor with shape [N x 1], the cross entropy loss.

    +
    Raises:

    ValueError – 1) the 1st dimension of input and label are not equal. +2) when soft_label == True, and the 2nd dimension of

    +
    +

    input and label are not equal.

    +
    +
      +
    1. when soft_label == False, and the 2nd dimension of +label is not 1.
      2. +
    +
    +

    Examples

    +
    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+
    +
    +
    + +
    +
    +

    square_error_cost

    +
    +
    +paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)
    +

    Square error cost layer

    +

    This layer accepts input predictions and target label and returns the +squared error cost.

    +

    For predictions, \(X\), and target labels, \(Y\), the equation is:

    +
    +\[Out = (X - Y)^2\]
    +

    In the above equation:

    +
    +
      +
    • \(X\): Input predictions, a tensor.
      • +
    • \(Y\): Input labels, a tensor.
      • +
    • \(Out\): Output value, same shape with \(X\).
      • +
    +
    + +++ + + + + + + + +
    参数:
      +
    • input (Variable) – Input tensor, has predictions.
      • +
    • label (Variable) – Label tensor, has target labels.
      • +
    +
    返回:

    +
    The tensor variable storing the element-wise squared error
    +

    difference of input and label.

    +
    +
    +

    +
    返回类型:

    Variable

    +
    +

    Examples

    +
    y = layers.data(name='y', shape=[1], dtype='float32')
+y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
+cost = layers.square_error_cost(input=y_predict, label=y)
+
    +
    +
    + +
    +
    +

    accuracy

    +
    +
    +paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)
    +

    This function computes the accuracy using the input and label. +The output is the top_k inputs and their indices.

    +
    + +
    +
    +

    chunk_eval

    +
    +
    +paddle.v2.fluid.layers.chunk_eval(input, label, chunk_scheme, num_chunk_types, excluded_chunk_types=None, **kwargs)
    +

    This function computes and outputs the precision, recall and +F1-score of chunk detection.

    +
    + +
    +
    +

    sequence_conv

    +
    +
    +paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None)
    +

    This function creates the op for sequence_conv, using the inputs and +other convolutional configurations for the filters and stride as given +in the input parameters to the function.

    +
    + +
    +
    +

    conv2d

    +
    +
    +paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=None, padding=None, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None)
    +

    Convlution2D Layer

    +

    The convolution2D layer calculates the output based on the input, filter +and strides, paddings, dilations, groups parameters. Input(Input) and +Output(Output) are in NCHW format. Where N is batch size, C is the number of +channels, H is the height of the feature, and W is the width of the feature. +The details of convolution layer, please refer UFLDL’s convolution, . +If bias attribution and activation type are provided, bias is added to the +output of the convolution, and the corresponding activation function is +applied to the final result.

    +

    For each input \(X\), the equation is:

    +
    +\[Out = \sigma (W \ast X + b)\]
    +

    In the above equation:

    +
      +
    • \(X\): Input value, a tensor with NCHW format.
      • +
    • \(W\): Filter value, a tensor with MCHW format.
      • +
    • \(\ast\): Convolution operation.
      • +
    • \(b\): Bias value, a 2-D tensor with shape [M, 1].
      • +
    • \(\sigma\): Activation function.
      • +
    • +
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      +
      different.
      +
      +
      • +
    +

    Example

    +
      +

    • Input:

      +

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      +

      Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

      +
      • +

    • Output: +Output shape: $(N, C_{out}, H_{out}, W_{out})$

      +
      • +
    +

    Where

    +
    +\[\]
    +

    H_{out}&= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \ +W_{out}&= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

    + +++ + + + + + + + + + +
    参数:
      +
    • input (Variable) – The input image with [N, C, H, W] format.
      • +
    • num_filters (int) – The number of filter. It is as same as the output +image channel.
      • +
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square.
      • +
    • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
      • +
    • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding. Default: padding = 0.
      • +
    • groups (int) – The groups number of the Conv2d Layer. According to grouped +convolution in Alex Krizhevsky’s Deep CNN paper: when group=2, +the first half of the filters is only connected to the first half +of the input channels, while the second half of the filters is only +connected to the second half of the input channels. Default: groups=1
      • +
    • param_attr (ParamAttr) – The parameters to the Conv2d Layer. Default: None
      • +
    • bias_attr (ParamAttr) – Bias parameter for the Conv2d layer. Default: None
      • +
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
      • +
    • act (str) – Activation type. Default: None
      • +
    +
    返回:

    +
    The tensor variable storing the convolution and
    +

    non-linearity activation result.

    +
    +
    +

    +
    返回类型:

    Variable

    +
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and +groups mismatch.

    +
    +

    Examples

    +
    data = fluid.layers.data(
+    name='data', shape=[3, 32, 32], dtype='float32')
+conv2d = fluid.layers.conv2d(
+    input=data, num_filters=2, filter_size=3, act="relu")
+
    +
    +
    + +
    +
    +

    sequence_pool

    +
    +
    +paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)
    +

    This function add the operator for sequence pooling. +It pools features of all time-steps of each instance, and is applied +on top of the input using pool_type mentioned in the parameters.

    +

    It supports four pool_type:

    +
      +
    • average: \(Out[i] = \frac{\sum_i X_i}{N}\)
      • +
    • sum: \(Out[i] = \sum_jX_{ij}\)
      • +
    • sqrt: \(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)
      • +
    • max: \(Out[i] = max(X_i)\)
      • +
    +
    x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+
+for different pool_type:
+  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
+  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
+  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
+             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
+  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
+
    +
    + +++ + + + + + +
    参数:
      +
    • input (variable) – The input variable which is a LoDTensor.
      • +
    • pool_type (string) – The pooling type of sequence_pool. +It supports average, sum, sqrt and max.
      • +
    +
    返回:

    The sequence pooling variable which is a Tensor.

    +
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
+sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
+sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
+max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
+
    +
    +
    + +
    +
    +

    pool2d

    +
    +
    +paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=None, pool_padding=None, global_pooling=False, use_cudnn=True, name=None)
    +

    This function adds the operator for pooling in 2 dimensions, using the +pooling configurations mentioned in input parameters.

    +
    + +
    +
    +

    batch_norm

    +
    +
    +paddle.v2.fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', name=None)
    +

    This function helps create an operator to implement +the BatchNorm layer using the configurations from the input parameters.

    +
    + +
    +
    +

    beam_search_decode

    +
    +
    +paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)
    +
    + +
    +
    +

    conv2d_transpose

    +
    +
    +paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
    +

    Convlution2D transpose layer

    +

    The convolution2D transpose layer calculates the output based on the input, +filter, and dilations, strides, paddings. Input(Input) and output(Output) +are in NCHW format. Where N is batch size, C is the number of channels, +H is the height of the feature, and W is the width of the feature. +Parameters(dilations, strides, paddings) are two elements. These two elements +represent height and width, respectively. The details of convolution transpose +layer, please refer to the following explanation and references +therein.

    +

    For each input \(X\), the equation is:

    +
    +\[Out = W \ast X\]
    +

    In the above equation:

    +
      +
    • \(X\): Input value, a tensor with NCHW format.
      • +
    • \(W\): Filter value, a tensor with MCHW format.
      • +
    • \(\ast\) : Convolution transpose operation.
      • +
    • +
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      +
      different.
      +
      +
      • +
    +

    Example

    +
      +

    • Input:

      +

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      +

      Filter shape: $(C_{in}, C_{out}, H_f, W_f)$

      +
      • +

    • Output:

      +

      Output shape: $(N, C_{out}, H_{out}, W_{out})$

      +
      • +
    +

    Where

    +
    +\[\begin{split}H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\ +W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]
    + +++ + + + + + + + + + +
    参数:
      +
    • input (Variable) – The input image with [N, C, H, W] format.
      • +
    • num_filters (int) – The number of the filter. It is as same as the output +image channel.
      • +
    • output_size (int|tuple|None) – The output image size. If output size is a +tuple, it must contain two integers, (image_H, image_W). This +parameter only works when filter_size is None.
      • +
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square. None if use output size to +calculate filter_size.
      • +
    • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding. Default: padding = 0.
      • +
    • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
      • +
    • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must +contain two integers, (dilation_H, dilation_W). Otherwise, the +dilation_H = dilation_W = dilation. Default: dilation = 1.
      • +
    • param_attr (ParamAttr) – The parameters to the Conv2d_transpose Layer. +Default: None
      • +
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    返回:

    The tensor variable storing the convolution transpose result.

    +
    返回类型:

    Variable

    +
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and +groups mismatch.

    +
    +

    Examples

    +
    data = fluid.layers.data(
+    name='data', shape=[3, 32, 32], dtype='float32')
+conv2d_transpose = fluid.layers.conv2d_transpose(
+    input=data, num_filters=2, filter_size=3)
+
    +
    +
    + +
    +
    +

    sequence_expand

    +
    +
    +paddle.v2.fluid.layers.sequence_expand(x, y, name=None)
    +

    Sequence Expand Layer. This layer will expand the input variable x +according to LoD information of y. And the following examples will +explain how sequence_expand works:

    +
    * Case 1
+    x is a LoDTensor:
+        x.lod = [[0,       2, 3],
+                 [0, 1,    3, 4]]
+        x.data = [a, b, c, d]
+        x.dims = [4, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0,    2,    4],
+                 [0, 3, 6, 7, 8]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 2-level LoDTensor:
+        out.lod = [[0,                2,    4],
+                   [0,       3,       6, 7, 8]]
+        out.data = [a, a, a, b, b, b, c, d]
+        out.dims = [8, 1]
+
+* Case 2
+    x is a Tensor:
+        x.data = [a, b, c]
+        x.dims = [3, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0, 2, 3, 6]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 1-level LoDTensor:
+        out.lod = [[0,    2, 3,      6]]
+        out.data = [a, a, b, c, c, c]
+        out.dims = [6, 1]
+
    +
    + +++ + + + + + + + +
    参数:
      +
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • y (Variable) – The input variable which is a LoDTensor.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    返回:

    The expanded variable which is a LoDTensor.

    +
    返回类型:

    Variable

    +
    +

    Examples

    +
    x = fluid.layers.data(name='x', shape=[10], dtype='float32')
+y = fluid.layers.data(name='y', shape=[10, 20],
+                 dtype='float32', lod_level=1)
+out = layers.sequence_expand(x=x, y=y)
+
    +
    +
    + +
    +
    +

    lstm_unit

    +
    +
    +paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None, name=None)
    +

    Lstm unit layer. The equation of a lstm step is:

    +
    +
    +\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
    +
    +

    The inputs of lstm unit include \(x_t\), \(h_{t-1}\) and +\(c_{t-1}\). The 2nd dimensions of \(h_{t-1}\) and \(c_{t-1}\) +should be same. The implementation separates the linear transformation and +non-linear transformation apart. Here, we take \(i_t\) as an example. +The linear transformation is applied by calling a fc layer and the +equation is:

    +
    +
    +\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i\]
    +
    +

    The non-linear transformation is applied by calling lstm_unit_op and the +equation is:

    +
    +
    +\[i_t = \sigma(L_{i_t})\]
    +
    +

    This layer has two outputs including \(h_t\) and \(o_t\).

    + +++ + + + + + + + + + +
    参数:
      +
    • x_t (Variable) – The input value of current step, a 2-D tensor with shape +M x N, M for batch size and N for input size.
      • +
    • hidden_t_prev (Variable) – The hidden value of lstm unit, a 2-D tensor +with shape M x S, M for batch size and S for size of lstm unit.
      • +
    • cell_t_prev (Variable) – The cell value of lstm unit, a 2-D tensor with +shape M x S, M for batch size and S for size of lstm unit.
      • +
    • forget_bias (float) – The forget bias of lstm unit.
      • +
    • param_attr (ParamAttr) – The attributes of parameter weights, used to set +initializer, name etc.
      • +
    • bias_attr (ParamAttr) – The attributes of bias weights, if not False, +bias weights will be created and be set to default value.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    返回:

    The hidden value and cell value of lstm unit.

    +
    返回类型:

    tuple

    +
    Raises:

    ValueError – The ranks of x_t, hidden_t_prev and cell_t_prev +not be 2 or the 1st dimensions of x_t, hidden_t_prev +and cell_t_prev not be the same or the 2nd dimensions of +hidden_t_prev and cell_t_prev not be the same.

    +
    +

    Examples

    +
    x_t = fluid.layers.fc(input=x_t_data, size=10)
+prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
+prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
+                                       hidden_t_prev=prev_hidden,
+                                       cell_t_prev=prev_cell)
+
    +
    +
    + +
    +
    +

    reduce_sum

    +
    +
    +paddle.v2.fluid.layers.reduce_sum(input, dim=None, keep_dim=False, name=None)
    +

    Computes the sum of tensor elements over the given dimension.

    + +++ + + + + + + + +
    参数:
      +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the sum is performed. If +None, sum all elements of input and return a +Tensor variable with a single element, otherwise must be in the +range \([-rank(input), rank(input))\). If \(dim < 0\), +the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    返回:

    The reduced Tensor variable.

    +
    返回类型:

    Variable

    +
    +

    Examples

    +
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_sum(x)  # [3.5]
+fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
+fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
+fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
+
    +
    +
    + +
    +
    +

    reduce_mean

    +
    +
    +paddle.v2.fluid.layers.reduce_mean(input, dim=None, keep_dim=False, name=None)
    +

    Computes the mean of tensor elements over the given dimension.

    + +++ + + + + + + + +
    参数:
      +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the mean is computed. If +None, compute the mean over all elements of input +and return a Tensor variable with a single element, otherwise +must be in the range \([-rank(input), rank(input))\). If +\(dim < 0\), the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    返回:

    The reduced Tensor variable.

    +
    返回类型:

    Variable

    +
    +

    Examples

    +
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_mean(x)  # [0.4375]
+fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
+fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
+fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
    -
    -

    accuracy

    -
    -
    -paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)
    -

    This function computes the accuracy using the input and label. -The output is the top_k inputs and their indices.

    -
    - -
    -
    -

    sequence_conv

    -
    -
    -paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None)
    -

    This function creates the op for sequence_conv, using the inputs and -other convolutional configurations for the filters and stride as given -in the input parameters to the function.

    -
    - -
    -
    -

    conv2d

    +
    +

    reduce_max

    -paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=None, padding=None, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None)
    -

    Convlution2D Layer

    -

    The convolution2D layer calculates the output based on the input, filter -and strides, paddings, dilations, groups parameters. Input(Input) and -Output(Output) are in NCHW format. Where N is batch size, C is the number of -channels, H is the height of the feature, and W is the width of the feature. -The details of convolution layer, please refer UFLDL’s convolution, . -If bias attribution and activation type are provided, bias is added to the -output of the convolution, and the corresponding activation function is -applied to the final result.

    -

    For each input \(X\), the equation is:

    -
    -\[Out = \sigma (W \ast X + b)\]
    -

    In the above equation:

    -
      -
    • \(X\): Input value, a tensor with NCHW format.
      • -
    • \(W\): Filter value, a tensor with MCHW format.
      • -
    • \(\ast\): Convolution operation.
      • -
    • \(b\): Bias value, a 2-D tensor with shape [M, 1].
      • -
    • \(\sigma\): Activation function.
      • -
    • -
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      -
      different.
      -
      -
      • -
    -

    Example

    -
      -

    • Input:

      -

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      -

      Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

      -
      • -

    • Output: -Output shape: $(N, C_{out}, H_{out}, W_{out})$

      -
      • -
    -

    Where

    -
    -\[\]
    -

    H_{out}&= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \ -W_{out}&= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

    +paddle.v2.fluid.layers.reduce_max(input, dim=None, keep_dim=False, name=None) +

    Computes the maximum of tensor elements over the given dimension.

    - - - -
    参数:
      -
    • input (Variable) – The input image with [N, C, H, W] format.
      • -
    • num_filters (int) – The number of filter. It is as same as the output -image channel.
      • -
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, -it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square.
      • -
    • stride (int|tuple) – The stride size. If stride is a tuple, it must -contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride. Default: stride = 1.
      • -
    • padding (int|tuple) – The padding size. If padding is a tuple, it must -contain two integers, (padding_H, padding_W). Otherwise, the -padding_H = padding_W = padding. Default: padding = 0.
      • -
    • groups (int) – The groups number of the Conv2d Layer. According to grouped -convolution in Alex Krizhevsky’s Deep CNN paper: when group=2, -the first half of the filters is only connected to the first half -of the input channels, while the second half of the filters is only -connected to the second half of the input channels. Default: groups=1
      • -
    • param_attr (ParamAttr) – The parameters to the Conv2d Layer. Default: None
      • -
    • bias_attr (ParamAttr) – Bias parameter for the Conv2d layer. Default: None
      • -
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn -library is installed. Default: True
      • -
    • act (str) – Activation type. Default: None
      • +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the maximum is computed. +If None, compute the maximum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    返回:

    -
    The tensor variable storing the convolution and
    -

    non-linearity activation result.

    -
    -
    -

    -
    返回类型:

    Variable

    +
    返回:

    The reduced Tensor variable.
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and -groups mismatch.

    +
    返回类型:

    Variable

    Examples

    -
    data = fluid.layers.data(
-    name='data', shape=[3, 32, 32], dtype='float32')
-conv2d = fluid.layers.conv2d(
-    input=data, num_filters=2, filter_size=3, act="relu")
+
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_max(x)  # [0.9]
+fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
+fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
+fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
    -
    -

    sequence_pool

    +
    +

    reduce_min

    -paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)
    -

    This function add the operator for sequence pooling. -It pools features of all time-steps of each instance, and is applied -on top of the input using pool_type mentioned in the parameters.

    -

    It supports four pool_type:

    -
      -
    • average: \(Out[i] = \frac{\sum_i X_i}{N}\)
      • -
    • sum: \(Out[i] = \sum_jX_{ij}\)
      • -
    • sqrt: \(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)
      • -
    • max: \(Out[i] = max(X_i)\)
      • -
    -
    x is a 1-level LoDTensor:
-  x.lod = [[0, 2, 5, 7]]
-  x.data = [1, 3, 2, 4, 6, 5, 1]
-  x.dims = [7, 1]
-
-then output is a Tensor:
-  out.dim = [3, 1]
-  with condition len(x.lod[-1]) - 1 == out.dims[0]
-
-for different pool_type:
-  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
-  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
-  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
-             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
-  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
-
    -
    +paddle.v2.fluid.layers.reduce_min(input, dim=None, keep_dim=False, name=None) +

    Computes the minimum of tensor elements over the given dimension.

    - + +
    参数:
      -
    • input (variable) – The input variable which is a LoDTensor.
      • -
    • pool_type (string) – The pooling type of sequence_pool. -It supports average, sum, sqrt and max.
      • +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • dim (int|None) – The dimension along which the minimum is computed. +If None, compute the minimum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • +
    • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    返回:

    The sequence pooling variable which is a Tensor.

    +
    返回:

    The reduced Tensor variable.

    +
    返回类型:

    Variable

    Examples

    -
    x = fluid.layers.data(name='x', shape=[7, 1],
-                 dtype='float32', lod_level=1)
-avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
-sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
-sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
-max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
+
    # x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_min(x)  # [0.1]
+fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
+fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
+fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
    -

    sequence_first_step

    +

    sequence_first_step

    paddle.v2.fluid.layers.sequence_first_step(input, **kwargs)
    @@ -1731,7 +2441,7 @@ then output is a Tensor:
    -

    sequence_last_step

    +

    sequence_last_step

    paddle.v2.fluid.layers.sequence_last_step(input, **kwargs)
    @@ -1752,95 +2462,47 @@ then output is a Tensor:
    参数:input (variable) – The input variable which is a LoDTensor.
    返回:The sequence’s last step variable which is a Tensor.
    -

    Examples

    -
    x = fluid.layers.data(name='x', shape=[7, 1],
-                 dtype='float32', lod_level=1)
-x_last_step = fluid.layers.sequence_last_step(input=x)
-
    -
    -
    - -
    -
    -

    pool2d

    -
    -
    -paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=None, pool_padding=None, global_pooling=False, use_cudnn=True, name=None)
    -

    This function adds the operator for pooling in 2 dimensions, using the -pooling configurations mentioned in input parameters.

    -
    - -
    -
    -

    batch_norm

    -
    -
    -paddle.v2.fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', name=None)
    -

    This function helps create an operator to implement -the BatchNorm layer using the configurations from the input parameters.

    -
    - -
    -
    -

    beam_search_decode

    -
    -
    -paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)
    -
    - -
    -
    -

    lod_rank_table

    -
    -
    -paddle.v2.fluid.layers.lod_rank_table(x, level=0)
    -

    LoD Rank Table Operator. Given an input variable x and a level number -of LoD, this layer creates a LodRankTable object. A LoDRankTable object -contains a list of bi-element tuples. Each tuple consists of an index and -a length, both of which are int type. Refering to specified level of LoD, -the index is the sequence index number and the length representes the -sequence length. Please note that the list is ranked in descending order by -the length. The following is an example:

    -
    -
    x is a LoDTensor:
-    x.lod = [[0,                2, 3],
-             [0,             5, 6, 7]]
-    x.data = [a, b, c, d, e, f, g]
-
-1. set level to 0:
-    Create lod rank table:
-        lod_rank_table_obj = lod_rank_table(x, level=0)
-
-    Get:
-        lod_rank_table_obj.items() = [(0, 2), (1, 1)]
-
-2. set level to 1:
-    Create lod rank table:
-        lod_rank_table_obj = lod_rank_table(x, level=1)
-
-    Get:
-        lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]
+
+返回:The sequence’s last step variable which is a Tensor.
+
+
+
+
    Examples
    
+
    x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+x_last_step = fluid.layers.sequence_last_step(input=x)
 
    
 
    
-
    +
    + +
    +
    +

    dropout

    +
    +
    +paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=None, **kwargs)
    +

    Computes dropout.

    +

    Drop or keep each element of x independently. Dropout is a regularization +technique for reducing overfitting by preventing neuron co-adaption during +training. The dropout operator randomly set (according to the given dropout +probability) the outputs of some units to zero, while others are remain +unchanged.

    -
    参数:
      -
    • x (Variable) – Input variable, a LoDTensor based which to create the lod -rank table.
      • -
    • level (int) – Specify the LoD level, on which to create the lod rank -table.
      • +
    • x (variable) – The input tensor.
      • +
    • dropout_prob (float) – Probability of setting units to zero.
      • +
    • is_test (bool) – A flag indicating whether it is in test phrase or not.
      • +
    • seed (int) – A Python integer used to create random seeds. If this +parameter is set to None, a random seed is used. +NOTE: If an integer seed is given, always the same output +units will be dropped. DO NOT use a fixed seed in training.
    返回:

    The created LoDRankTable object.

    +
    返回:

    A tensor variable.
    返回类型:

    Variable

    @@ -1849,74 +2511,116 @@ table.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10],
-                dtype='float32', lod_level=1)
-out = layers.lod_rank_table(x=x, level=0)
+
    x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
    -
    -

    max_sequence_len

    +
    +

    split

    -paddle.v2.fluid.layers.max_sequence_len(rank_table)
    -

    Max Sequence Len Operator. Given a LoDRankTable object, this layer -returns the max length of a batch of sequences. In fact, a LoDRankTable -object contains a list of tuples(<sequence index, sequence length>) and -the list is already sorted by sequence length in descending order, so the -operator just returns the sequence length of the first tuple element.

    +paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None) +

    Split the input tensor into multiple sub-tensors.

    - + - + - +
    参数:rank_table (Variable) – Input variable which is a LoDRankTable object.
    参数:
      +
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • num_or_sections (int|list) – If num_or_sections is an integer, +then the integer indicates the number of equal sized sub-tensors +that the tensor will be divided into. If num_or_sections +is a list of integers, the length of list indicates the number of +sub-tensors and the integers indicate the sizes of sub-tensors’ +dim dimension orderly.
      • +
    • dim (int) – The dimension along which to split. If \(dim < 0\), the +dimension to split along is \(rank(input) + dim\).
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
      • +
    +
    返回:The max length of sequence.
    返回:

    The list of segmented tensor variables.

    +
    返回类型:Variable
    返回类型:

    List

    +

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10],
-                dtype='float32', lod_level=1)
-rank_table = layers.lod_rank_table(x=x, level=0)
-max_seq_len = layers.max_sequence_len(rank_table)
+
    # x is a Tensor variable with shape [3, 9, 5]:
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
+x0.shape  # [3, 3, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 3, 5]
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
+x0.shape  # [3, 2, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 4, 5]
    -
    -

    topk

    +
    +

    ctc_greedy_decoder

    -paddle.v2.fluid.layers.topk(input, k)
    -

    topk

    -

    This function performs the operation that selects the k entries in the input -vector and outputs their values and indices as vectors. Thus topk_out[j] is -the j-th largest entry in input, and its index is topk_indices[j]

    +paddle.v2.fluid.layers.ctc_greedy_decoder(input, blank, name=None) +

    This op is used to decode sequences by greedy policy by below steps: +1. Get the indexes of max value for each row in input. a.k.a.

    +
    +
    numpy.argmax(input, axis=0).
    +
      +
    1. For each sequence in result of step1, merge repeated tokens between two +blanks and delete all blanks.
      2. +
    +

    A simple example as below:

    +
    Given:
+
+input.data = [[0.6, 0.1, 0.3, 0.1],
+              [0.3, 0.2, 0.4, 0.1],
+              [0.1, 0.5, 0.1, 0.3],
+              [0.5, 0.1, 0.3, 0.1],
+
+              [0.5, 0.1, 0.3, 0.1],
+              [0.2, 0.2, 0.2, 0.4],
+              [0.2, 0.2, 0.1, 0.5],
+              [0.5, 0.1, 0.3, 0.1]]
+
+input.lod = [[0, 4, 8]]
+
+Then:
+
+output.data = [[2],
+               [1],
+               [3]]
+
+output.lod = [[0, 2, 3]]
+
    +
    -
    参数:
      -
    • input (Variable|list) – The input tensor that has all the data.
      • -
    • k (int) – The number of top elements that the function will pick.
      • +
    • input (Variable) – (LoDTensor<float>), the probabilities of +variable-length sequences, which is a 2-D Tensor with +LoD information. It’s shape is [Lp, num_classes + 1], +where Lp is the sum of all input sequences’ length and +num_classes is the true number of classes. (not +including the blank label).
      • +
    • blank (int) – the blank label index of Connectionist Temporal +Classification (CTC) loss, which is in thehalf-opened +interval [0, num_classes + 1).
    返回:

    -
    The variable of type array that contains the k largest entries
    -

    from input.

    -
    -
    Variable: The variable of type array that contains the indices of k
    -

    largest entries from input.

    -
    -
    -

    +
    返回:

    CTC greedy decode result.
    返回类型:

    Variable

    @@ -1925,38 +2629,50 @@ the j-th largest entry in input, and its index is topk_indices[j]

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10])
-k = 5
-array = fluid.layers.topk(x, k)
+
    x = fluid.layers.data(name='x', shape=[8], dtype='float32')
+
+cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
    -
    -

    lod_tensor_to_array

    +
    +

    edit_distance

    -paddle.v2.fluid.layers.lod_tensor_to_array(x, table)
    -

    Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.

    +paddle.v2.fluid.layers.edit_distance(input, label, normalized=False, ignored_tokens=None, name=None) +

    EditDistance operator computes the edit distances between a batch of +hypothesis strings and their references. Edit distance, also called +Levenshtein distance, measures how dissimilar two strings are by counting +the minimum number of operations to transform one string into anthor. +Here the operations include insertion, deletion, and substitution.

    +

    For example, given hypothesis string A = “kitten” and reference +B = “sitting”, the edit distance is 3 for A will be transformed into B +at least after two substitutions and one insertion:

    +

    “kitten” -> “sitten” -> “sittin” -> “sitting”

    +

    Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with +the total number denoted by batch_size, and the separation is specified +by the LoD information. And the batch_size reference strings are arranged +in order in the same way in the LoDTensor Input(Refs).

    +

    Output(Out) contains the batch_size results and each stands for the edit +distance for a pair of strings respectively. If Attr(normalized) is true, +the edit distance will be divided by the length of reference string.

    -
    参数:
      -
    • x (Variable|list) – The LOD tensor to be converted to a LOD tensor array.
      • -
    • table (ParamAttr|list) – The variable that stores the level of lod -which is ordered by sequence length in -descending order.
      • +
    • input (Variable) – The indices for hypothesis strings.
      • +
    • label (Variable) – The indices for reference strings.
      • +
    • normalized (bool) – Indicated whether to normalize the edit distance by +the length of reference string.
      • +
    • ignored_tokens (list of int) – Tokens that should be removed before +calculating edit distance.
    返回:

    -
    The variable of type array that has been converted from a
    -

    tensor.

    -
    -
    -

    +
    返回:

    sequence-to-sequence edit distance in shape [batch_size, 1].
    返回类型:

    Variable

    @@ -1965,38 +2681,42 @@ descending order.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10])
-table = fluid.layers.lod_rank_table(x, level=0)
-array = fluid.layers.lod_tensor_to_array(x, table)
+
    x = fluid.layers.data(name='x', shape=[8], dtype='float32')
+y = fluid.layers.data(name='y', shape=[7], dtype='float32')
+
+cost = fluid.layers.edit_distance(input=x,label=y)
    -
    -

    array_to_lod_tensor

    +
    +

    l2_normalize

    -paddle.v2.fluid.layers.array_to_lod_tensor(x, table)
    -

    Convert a LoD_Tensor_Aarry to an LoDTensor.

    +paddle.v2.fluid.layers.l2_normalize(x, axis, epsilon=1e-12, name=None) +

    L2 normalize Layer

    +

    The l2 normalize layer normalizes x along dimension axis using an L2 +norm. For a 1-D tensor (dim is fixed to 0), this layer computes

    +

    output = x / sqrt(max(sum(x**2), epsilon))

    +

    For x with more dimensions, this layer independently normalizes each 1-D +slice along dimension axis.

    -
    参数:
      -
    • x (Variable|list) – The lod tensor array to be converted to a tensor.
      • -
    • table (ParamAttr|list) – The variable that stores the level of lod -which is ordered by sequence length in -descending order.
      • +
    • x (Variable|list) – The input tensor to l2_normalize layer.
      • +
    • axis (int) – Dimension along which to normalize the input.
      • +
    • epsilon (float) – A lower bound value for x‘s l2 norm. sqrt(epsilon) will +be used as the divisor if the l2 norm of x is less than +sqrt(epsilon).
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    返回:

    -
    The variable of type tensor that has been converted
    -

    from an array.

    -
    -
    -

    +
    返回:

    The output tensor variable.
    返回类型:

    Variable

    @@ -2005,37 +2725,59 @@ descending order.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10])
-table = fluid.layers.lod_rank_table(x, level=0)
-array = fluid.layers.lod_tensor_to_array(x, table)
-lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
+
    data = fluid.layers.data(name="data",
+                         shape=(3, 17, 13),
+                         dtype="float32")
+normed = fluid.layers.l2_normalize(x=data, axis=1)
    -
    -

    fill_constant

    +
    +

    matmul

    -paddle.v2.fluid.layers.fill_constant(shape, dtype, value, force_cpu=False, out=None)
    -

    fill_constant

    -

    This function creates a tensor with specified shape and dtype, and -initializes it with a constant specifed by value.

    -

    The attribute stop_gradient of the created tensor is set to True.

    +paddle.v2.fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, name=None) +

    Applies matrix multiplication to two tensors.

    +

    Currently, the input tensors’ rank can be any, but when the rank of any +inputs is bigger than 3, this two inputs’ rank should be equal.

    +

    The actual behavior depends on the shapes of \(x\), \(y\) and the +flag values of transpose_x, transpose_y. Specifically:

    +
      +
    • If a transpose flag is specified, the last two dimensions of the tensor +are transposed. If the tensor is rank-1 of shape \([D]\), then for +\(x\) it is treated as \([1, D]\) in nontransposed form and as +\([D, 1]\) in transposed form, whereas for \(y\) it is the +opposite: It is treated as \([D, 1]\) in nontransposed form and as +\([1, D]\) in transposed form.
      • +
    • After transpose, the two tensors are 2-D or n-D and matrix multiplication +performs in the following way.
        +
      • If both are 2-D, they are multiplied like conventional matrices.
        • +
      • If either is n-D, it is treated as a stack of matrices residing in the +last two dimensions and a batched matrix multiply supporting broadcast +applies on the two tensors.
        • +
      +
      • +
    +

    Also note that if the raw tensor \(x\) or \(y\) is rank-1 and +nontransposed, the prepended or appended dimension \(1\) will be +removed after matrix multiplication.

    -
    参数:
      -
    • shape (tuple|list|None) – Shape of the output tensor.
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of the output tensor.
      • -
    • value (float) – The constant value used to initialize the output tensor.
      • -
    • out (Variable) – The output tensor.
      • +
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • y (Variable) – The input variable which is a Tensor or LoDTensor.
      • +
    • transpose_x (bool) – Whether to transpose \(x\) before multiplication.
      • +
    • transpose_y (bool) – Whether to transpose \(y\) before multiplication.
      • +
    • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    返回:

    The tensor variable storing the output.

    +
    返回:

    The product Tensor variable.
    返回类型:

    Variable

    @@ -2044,37 +2786,70 @@ initializes it with a constant specifed by value.

    Examples

    -
    data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
+
    # Examples to clarify shapes of the inputs and output
+# x: [B, ..., M, K], y: [B, ..., K, N]
+fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
+
+# x: [B, M, K], y: [B, K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+
+# x: [B, M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+
+# x: [M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [M, N]
+
+# x: [B, M, K], y: [K]
+fluid.layers.matmul(x, y)  # out: [B, M]
+
+# x: [K], y: [K]
+fluid.layers.matmul(x, y)  # out: [1]
+
+# x: [M], y: [N]
+fluid.layers.matmul(x, y, True, True)  # out: [M, N]
    -
    -

    fill_constant_batch_size_like

    +
    +

    warpctc

    -paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0)
    -

    fill_constant_batch_size_like

    -

    This function creates a tensor of specified shape, dtype and batch size, -and initializes this with a constant supplied in value. The batch size is -obtained from the input tensor.

    -

    It also sets stop_gradient to True.

    +paddle.v2.fluid.layers.warpctc(input, label, blank=0, norm_by_times=False, **kwargs) +

    An operator integrating the open source Warp-CTC library +(https://github.com/baidu-research/warp-ctc) +to compute Connectionist Temporal Classification (CTC) loss. +It can be aliased as softmax with CTC, since a native softmax activation is +interated to the Warp-CTC library, to to normlize values for each row of the +input tensor.

    -
    参数:
      -
    • input (Variable) – Tensor whose dimensions will be used to get batch size
      • -
    • shape (tuple|list|None) – Shape of output tensor
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • -
    • value (float) – Constant value to initialize the output tensor
      • -
    • input_dim_idx (int) – Index of input’s batch size dimension
      • -
    • output_dim_idx (int) – Index of output’s batch size dimension
      • +
    • input (Variable) – (LodTensor, default: LoDTensor<float>), +the unscaled probabilities of variable-length sequences, +which is a 2-D Tensor with LoD information. +It’s shape is [Lp, num_classes + 1], where Lp is the sum of all input +sequences’ length and num_classes is the true number of classes. +(not including the blank label).
      • +
    • label (Variable) – (LodTensor, default: LoDTensor<int>), the ground truth +of variable-length sequence, which is a 2-D Tensor with LoD +information. It is of the shape [Lg, 1], where Lg is th sum of +all labels’ length.
      • +
    • blank – (int, default: 0), the blank label index of Connectionist +Temporal Classification (CTC) loss, which is in the +half-opened interval [0, num_classes + 1).
      • +
    • norm_by_times – (bool, default: false), whether to normalize
      • +
    • gradients by the number of time-step, which is also the (the) –
      • +
    • length. There is no need to normalize the gradients (sequence's) –
      • +
    • warpctc layer was follewed by a mean_op. (if) –
    返回:

    The tensor variable storing the output

    +
    返回:

    The Connectionist Temporal Classification (CTC) loss, +which is a 2-D Tensor of the shape [batch_size, 1].
    返回类型:

    Variable

    @@ -2083,33 +2858,49 @@ obtained from the input tensor.

    Examples

    -
    data = fluid.layers.fill_constant_batch_size_like(
-    input=like, shape=[1], value=0, dtype='int64')
-
    -
    -
    -

    ones

    +
    +

    sequence_reshape

    -paddle.v2.fluid.layers.ones(shape, dtype)
    -

    ones

    -

    This function creates a tensor of specified shape and -dtype, and initializes this with 1.

    -

    It also sets stop_gradient to True.

    +paddle.v2.fluid.layers.sequence_reshape(input, new_dim) +

    Sequence Reshape Layer

    +

    This layer will rearrange the input sequences. The new dimension is set by +user. Length of each sequence is computed according to original length, +original dimension and new dimension. The following example will help to +illustrate the function of this layer:

    +
    x is a LoDTensor:
+    x.lod  = [[0, 2, 6]]
+    x.data = [[1, 2], [3, 4],
+              [5, 6], [7, 8], [9, 10], [11, 12]]
+    x.dims = [6, 2]
+
+set new_dim = 4
+
+then out is a LoDTensor:
+    out.lod  = [[0, 1, 3]]
+    out.data = [[1, 2, 3, 4],
+                [5, 6, 7, 8], [9, 10, 11, 12]]
+    out.dims = [3, 4]
+
    +
    +

    Currently, only 1-level LoDTensor is supported and please make sure +(original length * original dimension) can be divided by new dimension with +no remainder for each sequence.

    -
    参数:
      -
    • shape (tuple|list|None) – Shape of output tensor
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • +
    • input (Variable) – (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor +with shape being [N, M] where M for dimension.
      • +
    • new_dim (int) – New dimension which the input LoDTensor is reshaped to.
    返回:

    The tensor variable storing the output

    +
    返回:

    Reshaped LoDTensor according to new dimension.
    返回类型:

    Variable

    @@ -2118,32 +2909,34 @@ obtained from the input tensor.

    Examples

    -
    data = fluid.layers.ones(shape=[1], dtype='int64')
+
    x = fluid.layers.data(name='x', shape=[5, 20],
+                  dtype='float32', lod_level=1)
+x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
    -
    -

    zeros

    +
    +

    transpose

    -paddle.v2.fluid.layers.zeros(shape, dtype)
    -

    zeros

    -

    This function creates a tensor of specified shape and -dtype, and initializes this with 0.

    -

    It also sets stop_gradient to True.

    +paddle.v2.fluid.layers.transpose(x, perm, name=None) +

    transpose Layer

    +

    Permute the dimensions of input according to perm.

    +

    The i-th dimension of the returned tensor will correspond to the +perm[i]-th dimension of input.

    -
    参数:
      -
    • shape (tuple|list|None) – Shape of output tensor
      • -
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • +
    • input (Variable) – (Tensor), A Tensor.
      • +
    • perm (list) – A permutation of the dimensions of input.
    返回:

    The tensor variable storing the output

    +
    返回:

    A transposed Tensor.
    返回类型:

    Variable

    @@ -2152,134 +2945,249 @@ obtained from the input tensor.

    Examples

    -
    data = fluid.layers.zeros(shape=[1], dtype='int64')
+
    x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
+x_transposed = layers.transpose(x, perm=[1, 0, 2])
    -
    -

    increment

    +
    +

    im2sequence

    -paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True)
    -

    This function performs an operation that increments each value in the -input \(x\) by an amount: \(value\) as mentioned in the input -parameter. This operation is performed in-place by default.

    +paddle.v2.fluid.layers.im2sequence(input, filter_size=1, stride=1, padding=0, name=None) +

    Extracts image patches from the input tensor to form a tensor of shape +{input.batch_size * output_height * output_width, filter_size_H * +filter_size_W * input.channels} which is similar with im2col. +This op use filter / kernel to scan images and convert these images to +sequences. After expanding, the number of time step are +output_height * output_width for an image, in which output_height and +output_width are calculated by below equation:

    +
    +\[output\_size = 1 + (2 * padding + img\_size - block\_size + stride - 1) / stride\]
    +

    And the dimension of each time step is block_y * block_x * input.channels.

    - -
    参数:
      -
    • x (Variable|list) – The tensor that has the input values.
      • -
    • value (float) – The amount by which the values should be incremented.
      • -
    • in_place (bool) – If the increment should be performed in-place.
      • +
    • input (Variable) – The input should be a tensor in NCHW format.
      • +
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square.
      • +
    • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
      • +
    • padding (int|tuple) – The padding size. If padding is a tuple, it can +contain two integers like (padding_H, padding_W) which means +padding_up = padding_down = padding_H and +padding_left = padding_right = padding_W. Or it can use +(padding_up, padding_left, padding_down, padding_right) to indicate +paddings of four direction. Otherwise, a scalar padding means +padding_up = padding_down = padding_left = padding_right = padding +Default: padding = 0.
      • +
    • name (int) – The name of this layer. It is optional.
    返回:

    -
    The tensor variable storing the transformation of
    -

    element-wise increment of each value in the input.

    -
    -
    -

    +
    返回:

    The output is a LoDTensor with shape +{input.batch_size * output_height * output_width, +filter_size_H * filter_size_W * input.channels}. +If we regard output as a matrix, each row of this matrix is +a step of a sequence.
    返回类型:

    Variable

    +
    返回类型:

    output
    -

    Examples

    -
    data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
-data = fluid.layers.increment(x=data, value=3.0, in_place=True)
+
    Examples:
    
+
    As an example:
    
+
    
+
    Given:
+
+x = [[[[ 6.  2.  1.]
+       [ 8.  3.  5.]
+       [ 0.  2.  6.]]
+
+      [[ 2.  4.  4.]
+       [ 6.  3.  0.]
+       [ 6.  4.  7.]]]
+
+     [[[ 6.  7.  1.]
+       [ 5.  7.  9.]
+       [ 2.  4.  8.]]
+
+      [[ 1.  2.  1.]
+       [ 1.  3.  5.]
+       [ 9.  0.  8.]]]]
+
+x.dims = {2, 2, 3, 3}
+
+And:
+
+filter = [2, 2]
+stride = [1, 1]
+padding = [0, 0]
+
+Then:
+
+output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
+               [ 2.  1.  3.  5.  4.  4.  3.  0.]
+               [ 8.  3.  0.  2.  6.  3.  6.  4.]
+               [ 3.  5.  2.  6.  3.  0.  4.  7.]
+               [ 6.  7.  5.  7.  1.  2.  1.  3.]
+               [ 7.  1.  7.  9.  2.  1.  3.  5.]
+               [ 5.  7.  2.  4.  1.  3.  9.  0.]
+               [ 7.  9.  4.  8.  3.  5.  0.  8.]]
+
+output.dims = {8, 9}
+
+output.lod = [[0, 4, 8]]
+
    
+
    
+
    The simple usage is:
    
+
    output = fluid.layers.im2sequence(
+    input=layer, stride=[1, 1], filter_size=[2, 2])
 
    
 
    
+
    -
    -

    array_write

    +
    +

    nce

    -paddle.v2.fluid.layers.array_write(x, i, array=None)
    -

    This function writes the given input variable to the specified position -indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the -output LOD_TENSOR_ARRAY is not given(None), a new one will be created and -returned.

    +paddle.v2.fluid.layers.nce(input, label, num_total_classes, sample_weight=None, param_attr=None, bias_attr=None, num_neg_samples=None) +

    Compute and return the noise-contrastive estimation training loss. +See [Noise-contrastive estimation: A new estimation principle for unnormalized statistical models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf). +By default this operator uses a uniform distribution for sampling.

    - - -
    参数:
      -
    • x (Variable|list) – The input tensor from which the data will be read.
      • -
    • i (Variable|list) – The index of the output LOD_TENSOR_ARRAY, pointing to -the position to which the input tensor will be -written.
      • -
    • array (Variable|list) – The output LOD_TENSOR_ARRAY to which the input -tensor will be written. If this parameter is -NONE, a new LOD_TENSOR_ARRAY will be created and -returned.
      • +
    • input – (Tensor) A tensor of shape [batch_size, dim]. +Duplicable: False Optional: False
      • +
    • label – (Tensor) A tensor of shape [batch_size, num_true_class]. ‘num_true_class’ is the number of target classes in each sample.The number of target classes per sample should be same. If you have a variable number of target classes, you can pad them out to a constant number by either repeating them or by padding with an otherwise unused class.) +Duplicable: False Optional: False
      • +
    • weight – (Tensor) A tensor of shape [num_class, dim]. ‘num_class’ is the total number of class. +Duplicable: False Optional: False
      • +
    • bias – (Tensor) A tensor of shape [num_class, 1]. ‘num_class’ is the total number of class. It is a dispensable input. +Duplicable: False Optional: True
      • +
    • sample_weight – (Tensor) A tensor of shape [batch_size, 1] storing a weight for each sample. And it is a dispensable input. The default value of sample is 1. +Duplicable: False Optional: True
      • +
    • num_total_classes (INT) – Total number of classes in all samples.
      • +
    • num_neg_samples (INT) – The number of negative classes. The default value is 10.
      • +
    • custom_neg_classes (INTS) – This attribute only be used in unitest. Classes in this list wiil be used as negative classes for every samples. Under normal conditions, user should avoid setting this attribute.
    返回:

    The output LOD_TENSOR_ARRAY where the input tensor is written.

    -
    返回类型:

    Variable

    +
    返回:

    (Tensor) A tensor of shape [batch_size, 1]. Cost of samples.
    -

    Examples

    -
    -

    create_array

    + +
    +

    row_conv

    +
    +
    +paddle.v2.fluid.layers.row_conv(input, future_context_size, param_attr=None, act=None)
    +

    Row Conv Operator. This layer will apply lookahead convolution to +input. The input variable should be a 2D LoDTensor with shape [T, D]. +Parameters with shape [future_context_size + 1, D] will be created. The math +equation of row convolution is as follows:

    +
    +\[Out_{i} = \sum_{j = i} ^ {i + \tau} X_{j} \odot W_{i - j}\]
    +

    In the above equation:

    +
      +
    • \(Out_{i}\): The i-th row of output variable with shape [1, D].
      • +
    • \(\tau\): Future context size.
      • +
    • \(X_{j}\): The j-th row of input variable with shape [1, D].
      • +
    • \(W_{i-j}\): The (i-j)-th row of parameters with shape [1, D].
      • +
    +

    More details about row_conv please refer to the paper (http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and +the design document (https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).

    - + - + - +
    参数:dtype (int|float) – The data type of the elements in the array.
    参数:
      +
    • input (Variable) – Input variable, a 2D LoDTensor with shape [T, D].
      • +
    • future_context_size (int) – Future context size. Please note, the shape +of convolution kernel is [future_context_size + 1, D].
      • +
    • param_attr (ParamAttr) – Attributes of parameters, including +name, initializer etc.
      • +
    • act (str) – Non-linear activation to be applied to output variable.
      • +
    +
    返回:The tensor variable storing the elements of data type.
    返回:

    The output tensor with same shape as input tensor.

    +
    返回类型:Variable
    返回类型:

    Variable

    +

    Examples

    -
    data = fluid.layers.create_array(dtype='float32')
+
    x = fluid.layers.data(name='x', shape=[16],
+                dtype='float32', lod_level=1)
+out = fluid.layers.row_conv(input=x, future_context_size=2)
    -
    -

    less_than

    +
    +

    multiplex

    -paddle.v2.fluid.layers.less_than(x, y, cond=None, **ignored)
    -

    Less than

    -

    This layer returns the truth value of \(x < y\) elementwise.

    +paddle.v2.fluid.layers.multiplex(inputs, index) +

    Multiplex Layer

    +

    Referring to the given index variable, this layer selects rows from the +input variables to construct a multiplex variable. Assuming that there are +\(m\) input variables and \(I_i\) represents the i-th input +variable and \(i\) is in [0, \(m\)). All input variables are +tensors with same shape [\(d_0\), \(d_1\), ..., \(d_R\)]. +Please note that rank of the input tensor should be at least 2. Each input +variable will be treated as a 2-D matrix with shape [\(M\), \(N\)] +where \(M\) for \(d_0\) and \(N\) for \(d_1\) * \(d_2\) +* ... * \(d_R\). Let \(I_i[j]\) be the j-th row of the i-th input +variable. The given index variable should be a 2-D tensor with shape +[\(M\), 1]. Let ID[i] be the i-th index value of the index variable. +Then the output variable will be a tensor with shape [\(d_0\), +\(d_1\), ..., \(d_R\)]. If we treat the output tensor as a 2-D +matrix with shape [\(M\), \(N\)] and let \(O[i]\) be the i-th +row of the matrix, then O[i] is equal to \(I_{ID[i]}[i]\).

    -
    参数:
      -
    • x (Variable) – First operand of less_than
      • -
    • y (Variable) – Second operand of less_than
      • -
    • cond (Variable|None) – Optional output variable to store the result of less_than
      • +
    • inputs (list) – A list of variables to gather from. All variables have the +same shape and the rank is at least 2.
      • +
    • index (Variable) – Tensor<int32>, index variable which is a 2-D tensor +with shape [M, 1] where M is the batch size.
    返回:

    The tensor variable storing the output of less_than.

    +
    返回:

    Multiplex variable gathered from input variables.
    返回类型:

    Variable

    @@ -2288,721 +3196,664 @@ LayerHelper.

    Examples

    -
    less = fluid.layers.less_than(x=label, y=limit)
+
    x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
+x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
+index = fluid.layers.data(name='index', shape=[1], dtype='int32')
+out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
    -
    -

    array_read

    +
    +
    +

    ops

    +
    +

    mean

    -paddle.v2.fluid.layers.array_read(array, i)
    -

    This function performs the operation to read the data in as an -LOD_TENSOR_ARRAY. -:param array: The input tensor that will be written to an array. -:type array: Variable|list -:param i: The subscript index in tensor array, that points the

    -
    -
    place where data will be written to.
    +paddle.v2.fluid.layers.mean(**kwargs) +

    Mean Operator.

    +

    Out is a scalar which is the mean of all elements in X.

    - + - +
    返回:The tensor type variable that has the data written to it.
    参数:x – The input of mean op +Duplicable: False Optional: False
    返回类型:Variable
    返回:The output of mean op
    -

    Examples

    -
    - -
    -
    -

    shrink_memory

    -
    -
    -paddle.v2.fluid.layers.shrink_memory(x, i, table)
    -

    This function creates an operator to shrink_rnn_memory using the RankTable -as mentioned in the input parameter.

    -
    -

    array_length

    +
    +

    mul

    -paddle.v2.fluid.layers.array_length(array)
    -

    This function performs the operation to find the length of the input -LOD_TENSOR_ARRAY.

    +paddle.v2.fluid.layers.mul(**kwargs) +

    Mul Operator.

    +

    This operator is used to perform matrix multiplication for input $X$ and $Y$.

    +

    The equation is:

    +

    $$Out = X * Y$$

    +

    Both the input $X$ and $Y$ can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input $X$.

    - - - + - +
    参数:array (LOD_TENSOR_ARRAY) – The input array that will be used -to compute the length.
    返回:The length of the input LoDTensorArray.
    参数:
      +
    • x – (Tensor), The first input tensor of mul op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of mul op. +Duplicable: False Optional: False
      • +
    • x_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two +dimensions as its inputs. If the input $X$ is a tensor with more +than two dimensions, $X$ will be flattened into a two-dimensional +matrix first. The flattening rule is: the first num_col_dims +will be flattened to form the first dimension of the final matrix +(the height of the matrix), and the rest rank(X) - num_col_dims +dimensions are flattened to form the second dimension of the final +matrix (the width of the matrix). As a result, height of the +flattened matrix is equal to the product of $X$’s first +x_num_col_dims dimensions’ sizes, and width of the flattened +matrix is equal to the product of $X$’s last rank(x) - num_col_dims +dimensions’ size. For example, suppose $X$ is a 6-dimensional +tensor with the shape [2, 3, 4, 5, 6], and x_num_col_dims = 3. +Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = +[24, 30].
      • +
    • y_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two, +dimensions as its inputs. If the input $Y$ is a tensor with more +than two dimensions, $Y$ will be flattened into a two-dimensional +matrix first. The attribute y_num_col_dims determines how $Y$ is +flattened. See comments of x_num_col_dims for more details.
      • +
    +
    返回类型:Variable
    返回:

    (Tensor), The output tensor of mul op.

    +
    -

    Examples

    -
    -

    conv2d_transpose

    +
    +

    reshape

    -paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
    -

    Convlution2D transpose layer

    -

    The convolution2D transpose layer calculates the output based on the input, -filter, and dilations, strides, paddings. Input(Input) and output(Output) -are in NCHW format. Where N is batch size, C is the number of channels, -H is the height of the feature, and W is the width of the feature. -Parameters(dilations, strides, paddings) are two elements. These two elements -represent height and width, respectively. The details of convolution transpose -layer, please refer to the following explanation and references -therein.

    -

    For each input \(X\), the equation is:

    -
    -\[Out = W \ast X\]
    -

    In the above equation:

    -
      -
    • \(X\): Input value, a tensor with NCHW format.
      • -
    • \(W\): Filter value, a tensor with MCHW format.
      • -
    • \(\ast\) : Convolution transpose operation.
      • -
    • -
      \(Out\): Output value, the shape of \(Out\) and \(X\) may be
      -
      different.
      -
      -
      • -
    -

    Example

    -
      -

    • Input:

      -

      Input shape: $(N, C_{in}, H_{in}, W_{in})$

      -

      Filter shape: $(C_{in}, C_{out}, H_f, W_f)$

      -
      • -

    • Output:

      -

      Output shape: $(N, C_{out}, H_{out}, W_{out})$

      -
      • -
    -

    Where

    -
    -\[\begin{split}H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\ -W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]
    +paddle.v2.fluid.layers.reshape(**kwargs) +

    Reshape Operator.

    +

    Reshape Input(X) into the shape specified by Attr(shape).

    +

    An example: +Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]

    +

    and target shape = [1, 4], the reshape operator will transform +the tensor X into a 2-D tensor: [[1, 2, 3, 4]]

    +

    One dimension in the target shape can be set -1, representing that its +size is unknown. In this case, the real dimension will be infered from +the original shape of Input(X) and other dimensions in the target shape.

    - - - - -
    参数:
      -
    • input (Variable) – The input image with [N, C, H, W] format.
      • -
    • num_filters (int) – The number of the filter. It is as same as the output -image channel.
      • -
    • output_size (int|tuple|None) – The output image size. If output size is a -tuple, it must contain two integers, (image_H, image_W). This -parameter only works when filter_size is None.
      • -
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, -it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square. None if use output size to -calculate filter_size.
      • -
    • padding (int|tuple) – The padding size. If padding is a tuple, it must -contain two integers, (padding_H, padding_W). Otherwise, the -padding_H = padding_W = padding. Default: padding = 0.
      • -
    • stride (int|tuple) – The stride size. If stride is a tuple, it must -contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride. Default: stride = 1.
      • -
    • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must -contain two integers, (dilation_H, dilation_W). Otherwise, the -dilation_H = dilation_W = dilation. Default: dilation = 1.
      • -
    • param_attr (ParamAttr) – The parameters to the Conv2d_transpose Layer. -Default: None
      • -
    • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn -library is installed. Default: True
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – The input tensor of reshape operator. +Duplicable: False Optional: False
      • +
    • shape (INTS) – (vector<int>) Target shape of reshape operator.
    返回:

    The tensor variable storing the convolution transpose result.

    -
    返回类型:

    Variable

    -
    Raises:

    ValueError – If the shapes of input, filter_size, stride, padding and -groups mismatch.

    +
    返回:

    The output tensor of reshape operator.
    -

    Examples

    -
    data = fluid.layers.data(
-    name='data', shape=[3, 32, 32], dtype='float32')
-conv2d_transpose = fluid.layers.conv2d_transpose(
-    input=data, num_filters=2, filter_size=3)
-
    -
    -
    - -
    -
    -

    sequence_expand

    -
    -
    -paddle.v2.fluid.layers.sequence_expand(x, y, name=None)
    -

    Sequence Expand Layer. This layer will expand the input variable x -according to LoD information of y. And the following examples will -explain how sequence_expand works:

    -
    * Case 1
-    x is a LoDTensor:
-        x.lod = [[0,       2, 3],
-                 [0, 1,    3, 4]]
-        x.data = [a, b, c, d]
-        x.dims = [4, 1]
-
-    y is a LoDTensor:
-        y.lod = [[0,    2,    4],
-                 [0, 3, 6, 7, 8]]
-
-    with condition len(y.lod[-1]) - 1 == x.dims[0]
-
-    then output is a 2-level LoDTensor:
-        out.lod = [[0,                2,    4],
-                   [0,       3,       6, 7, 8]]
-        out.data = [a, a, a, b, b, b, c, d]
-        out.dims = [8, 1]
-
-* Case 2
-    x is a Tensor:
-        x.data = [a, b, c]
-        x.dims = [3, 1]
-
-    y is a LoDTensor:
-        y.lod = [[0, 2, 3, 6]]
-
-    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
    - then output is a 1-level LoDTensor: - out.lod = [[0, 2, 3, 6]] - out.data = [a, a, b, c, c, c] - out.dims = [6, 1] -
    +
    +

    scale

    +
    +
    +paddle.v2.fluid.layers.scale(**kwargs)
    +

    Scale operator

    +

    $$Out = scale*X$$

    - - -
    参数:
      -
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • y (Variable) – The input variable which is a LoDTensor.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor) Input tensor of scale operator. +Duplicable: False Optional: False
      • +
    • scale (FLOAT) – (float, default 1.0)The scaling factor of the scale operator.
    返回:

    The expanded variable which is a LoDTensor.

    -
    返回类型:

    Variable

    +
    返回:

    (Tensor) Output tensor of scale operator.
    -

    Examples

    -
    x = fluid.layers.data(name='x', shape=[10], dtype='float32')
-y = fluid.layers.data(name='y', shape=[10, 20],
-                 dtype='float32', lod_level=1)
-out = layers.sequence_expand(x=x, y=y)
-
    -
    -
    -

    gru_unit

    +
    +

    sigmoid_cross_entropy_with_logits

    -paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')
    -

    GRU unit layer. The equation of a gru step is:

    +paddle.v2.fluid.layers.sigmoid_cross_entropy_with_logits(**kwargs) +

    SigmoidCrossEntropyWithLogits Operator.

    +

    This measures the element-wise probability error in classification tasks +in which each class is independent. This can be thought of as predicting labels +for a data-point, where labels are not mutually exclusive. +For example, a news article can be about politics, technology or sports +at the same time or none of these.

    +

    The logistic loss is given as follows:

    -
    -\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)\\h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})\end{aligned}\end{align} \]
    -
    -

    The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms -of the equation above, the \(z_t\) is split into 3 parts - -\(xu_t\), \(xr_t\) and \(xm_t\). This means that in order to -implement a full GRU unit operator for an input, a fully -connected layer has to be applied, such that \(z_t = W_{fc}x_t\).

    -

    The terms \(u_t\) and \(r_t\) represent the update and reset gates -of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is -an intermediate candidate hidden output, which is denoted by \(m_t\). -This layer has three outputs \(h_t\), \(dot(r_t, h_{t-1})\) -and concatenation of \(u_t\), \(r_t\) and \(m_t\).

    +
    $$loss = -Labels * log(sigma(X)) - (1 - Labels) * log(1 - sigma(X))$$
    +

    We know that $$sigma(X) = (1 / (1 + exp(-X)))$$. By substituting this we get:

    +
    +
    $$loss = X - X * Labels + log(1 + exp(-X))$$
    +

    For stability and to prevent overflow of $$exp(-X)$$ when X < 0, +we reformulate the loss as follows:

    +
    +
    $$loss = max(X, 0) - X * Labels + log(1 + exp(-|X|))$$
    +

    Both the input X and Labels can carry the LoD (Level of Details) information. +However the output only shares the LoD with input X.

    - - -
    参数:
      -
    • input (Variable) – The fc transformed input value of current step.
      • -
    • hidden (Variable) – The hidden value of lstm unit from previous step.
      • -
    • size (integer) – The input dimension value.
      • -
    • weight (ParamAttr) – The weight parameters for gru unit. Default: None
      • -
    • bias (ParamAttr) – The bias parameters for gru unit. Default: None
      • -
    • activation (string) – The activation type for cell (actNode). -Default: ‘tanh’
      • -
    • gate_activation (string) – The activation type for gates (actGate). -Default: ‘sigmoid’
      • +
    • x – (Tensor, default Tensor<float>), a 2-D tensor with shape N x D, where N is the batch size and D is the number of classes. This input is a tensor of logits computed by the previous operator. Logits are unscaled log probabilities given as log(p/(1-p)). +Duplicable: False Optional: False
      • +
    • label – (Tensor, default Tensor<float>), a 2-D tensor of the same type and shape as X. This input is a tensor of probabalistic labels for each logit +Duplicable: False Optional: False
    返回:

    The hidden value, reset-hidden value and gate values.

    -
    返回类型:

    tuple

    +
    返回:

    (Tensor, default Tensor<float>), a 2-D tensor with shape N x D of elementwise logistic losses.
    -

    Examples

    -
    # assuming we have x_t_data and prev_hidden of size=10
-x_t = fluid.layers.fc(input=x_t_data, size=30)
-hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
-                                       hidden = prev_hidden)
+
    + +
    +
    +

    elementwise_add

    +
    +
    +paddle.v2.fluid.layers.elementwise_add(**kwargs)
    +

    Limited Elementwise Add Operator.

    +

    The equation is:

    +

    $$Out = X + Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    + +++ + + + + + +
    参数:
      +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    +
    返回:

    The output of elementwise op.

    +
    -
    -

    lstm_unit

    +
    +

    elementwise_div

    -paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None, name=None)
    -

    Lstm unit layer. The equation of a lstm step is:

    -
    -
    -\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
    -
    -

    The inputs of lstm unit include \(x_t\), \(h_{t-1}\) and -\(c_{t-1}\). The 2nd dimensions of \(h_{t-1}\) and \(c_{t-1}\) -should be same. The implementation separates the linear transformation and -non-linear transformation apart. Here, we take \(i_t\) as an example. -The linear transformation is applied by calling a fc layer and the -equation is:

    -
    -
    -\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i\]
    -
    -

    The non-linear transformation is applied by calling lstm_unit_op and the -equation is:

    -
    -
    -\[i_t = \sigma(L_{i_t})\]
    -
    -

    This layer has two outputs including \(h_t\) and \(o_t\).

    +paddle.v2.fluid.layers.elementwise_div(**kwargs) +

    Limited Elementwise Div Operator.

    +

    The equation is:

    +

    $$Out = X / Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - +
    参数:
      -
    • x_t (Variable) – The input value of current step, a 2-D tensor with shape -M x N, M for batch size and N for input size.
      • -
    • hidden_t_prev (Variable) – The hidden value of lstm unit, a 2-D tensor -with shape M x S, M for batch size and S for size of lstm unit.
      • -
    • cell_t_prev (Variable) – The cell value of lstm unit, a 2-D tensor with -shape M x S, M for batch size and S for size of lstm unit.
      • -
    • forget_bias (float) – The forget bias of lstm unit.
      • -
    • param_attr (ParamAttr) – The attributes of parameter weights, used to set -initializer, name etc.
      • -
    • bias_attr (ParamAttr) – The attributes of bias weights, if not False, -bias weights will be created and be set to default value.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    返回:

    The hidden value and cell value of lstm unit.

    +
    返回:

    The output of elementwise op.
    返回类型:

    tuple

    +
    +
    + +
    +
    +

    elementwise_sub

    +
    +
    +paddle.v2.fluid.layers.elementwise_sub(**kwargs)
    +

    Limited Elementwise Sub Operator.

    +

    The equation is:

    +

    $$Out = X - Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    + +++ + -
    参数:
      +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    Raises:

    ValueError – The ranks of x_t, hidden_t_prev and cell_t_prev -not be 2 or the 1st dimensions of x_t, hidden_t_prev -and cell_t_prev not be the same or the 2nd dimensions of -hidden_t_prev and cell_t_prev not be the same.

    +
    返回:

    The output of elementwise op.
    -

    Examples

    -
    x_t = fluid.layers.fc(input=x_t_data, size=10)
-prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
-prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
-hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
-                                       hidden_t_prev=prev_hidden,
-                                       cell_t_prev=prev_cell)
+
    + +
    +
    +

    elementwise_mul

    +
    +
    +paddle.v2.fluid.layers.elementwise_mul(**kwargs)
    +

    Limited Elementwise Mul Operator.

    +

    The equation is:

    +

    $$Out = X odotY$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
    -
    - -
    -
    -

    sequence_softmax

    -
    -
    -paddle.v2.fluid.layers.sequence_softmax(**kwargs)
    -

    Sequence Softmax Operator.

    -

    SequenceSoftmaxOp computes the softmax activation among all time-steps for each -sequence. The dimension of each time-step should be 1. Thus, the shape of -input Tensor can be either [N, 1] or [N], where N is the sum of the length -of all sequences.

    -

    The algorithm works as follows:

    -
    -
    for i-th sequence in a mini-batch:
    -

    $$ -Out(X[lod[i]:lod[i+1]], :) = frac{exp(X[lod[i]:lod[i+1], :])} {sum(exp(X[lod[i]:lod[i+1], :]))} -$$

    -

    For example, for a mini-batch of 3 sequences with variable-length, -each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], -then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] -and N turns out to be 7.

    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - + - +
    参数:x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. -Duplicable: False Optional: False
    参数:
      +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
      • +
    +
    返回:(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.
    返回:

    The output of elementwise op.

    +
    -
    -

    reduce_sum

    +
    +

    elementwise_max

    -paddle.v2.fluid.layers.reduce_sum(input, dim=None, keep_dim=False, name=None)
    -

    Computes the sum of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.elementwise_max(**kwargs) +

    Limited Elementwise Max Operator.

    +

    The equation is:

    +

    $$Out = max(X, Y)$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - -
    参数:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the sum is performed. If -None, sum all elements of input and return a -Tensor variable with a single element, otherwise must be in the -range \([-rank(input), rank(input))\). If \(dim < 0\), -the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    返回:

    The reduced Tensor variable.

    -
    返回类型:

    Variable

    +
    返回:

    The output of elementwise op.
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_sum(x)  # [3.5]
-fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
-fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
-fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
-
    -
    -
    -

    reduce_mean

    +
    +

    elementwise_min

    -paddle.v2.fluid.layers.reduce_mean(input, dim=None, keep_dim=False, name=None)
    -

    Computes the mean of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.elementwise_min(**kwargs) +

    Limited Elementwise Max Operator.

    +

    The equation is:

    +

    $$Out = min(X, Y)$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - -
    参数:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the mean is computed. If -None, compute the mean over all elements of input -and return a Tensor variable with a single element, otherwise -must be in the range \([-rank(input), rank(input))\). If -\(dim < 0\), the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    返回:

    The reduced Tensor variable.

    -
    返回类型:

    Variable

    +
    返回:

    The output of elementwise op.
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_mean(x)  # [0.4375]
-fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
-fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
-fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
-
    -
    -
    -

    reduce_max

    +
    +

    elementwise_pow

    -paddle.v2.fluid.layers.reduce_max(input, dim=None, keep_dim=False, name=None)
    -

    Computes the maximum of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.elementwise_pow(**kwargs) +

    Limited Elementwise Pow Operator.

    +

    The equation is:

    +

    $$Out = X ^ Y$$

    +

    $X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be +smaller than or equal to the dimensions of $X$.

    +

    There are two cases for this operator: +1. The shape of $Y$ is same with $X$; +2. The shape of $Y$ is a subset of $X$.

    +

    For case 2: +$Y$ will be broadcasted to match the shape of $X$ and axis should be +set to index of the start dimension to broadcast $Y$ onto $X$.

    +
    +
    For example
    +
    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
    +
    +
    +
    +

    Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

    - - -
    参数:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the maximum is computed. -If None, compute the maximum over all elements of -input and return a Tensor variable with a single element, -otherwise must be in the range \([-rank(input), rank(input))\). -If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
      • +
    • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    返回:

    The reduced Tensor variable.

    -
    返回类型:

    Variable

    +
    返回:

    The output of elementwise op.
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_max(x)  # [0.9]
-fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
-fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
-fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
-
    -
    -
    -

    reduce_min

    +
    +

    clip

    -paddle.v2.fluid.layers.reduce_min(input, dim=None, keep_dim=False, name=None)
    -

    Computes the minimum of tensor elements over the given dimension.

    +paddle.v2.fluid.layers.clip(**kwargs) +

    Clip Operator.

    +

    The clip operator limits the value of given input within an interval. The +interval is specified with arguments ‘min’ and ‘max’:

    +

    $$ +Out = min(max(X, min), max) +$$

    - - -
    参数:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • dim (int|None) – The dimension along which the minimum is computed. -If None, compute the minimum over all elements of -input and return a Tensor variable with a single element, -otherwise must be in the range \([-rank(input), rank(input))\). -If \(dim < 0\), the dimension to reduce is \(rank + dim\).
      • -
    • keep_dim (bool) – Whether to reserve the reduced dimension in the -output Tensor. The result tensor will have one fewer dimension -than the input unless keep_dim is true.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor)The input of clip op.The number of dimensions must be between [1, 9]. +Duplicable: False Optional: False
      • +
    • min (FLOAT) – (float)Minimum value, under which element is replaced by min.
      • +
    • max (FLOAT) – (float)Maximum value, above which element is replaced by max
    返回:

    The reduced Tensor variable.

    -
    返回类型:

    Variable

    +
    返回:

    (Tensor)The output of clip op with shape as input(X)
    -

    Examples

    -
    # x is a Tensor variable with following elements:
-#    [[0.2, 0.3, 0.5, 0.9]
-#     [0.1, 0.2, 0.6, 0.7]]
-# Each example is followed by the correspending output tensor.
-fluid.layers.reduce_min(x)  # [0.1]
-fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
-fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
-fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
-
    -
    -
    -

    split

    +
    +

    clip_by_norm

    -paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None)
    -

    Split the input tensor into multiple sub-tensors.

    +paddle.v2.fluid.layers.clip_by_norm(**kwargs) +

    ClipByNorm Operator.

    +

    This operator limits the L2 norm of the input $X$ within $max_norm$. +If the L2 norm of $X$ is less than or equal to $max_norm$, $Out$ will be +the same as $X$. If the L2 norm of $X$ is greater than $max_norm$, $X$ will +be linearly scaled to make the L2 norm of $Out$ equal to $max_norm$, as +shown in the following formula:

    +

    $$ +Out = frac{max_norm * X}{norm(X)}, +$$

    +

    where $norm(X)$ represents the L2 norm of $X$.

    - - -
    参数:
      -
    • input (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • num_or_sections (int|list) – If num_or_sections is an integer, -then the integer indicates the number of equal sized sub-tensors -that the tensor will be divided into. If num_or_sections -is a list of integers, the length of list indicates the number of -sub-tensors and the integers indicate the sizes of sub-tensors’ -dim dimension orderly.
      • -
    • dim (int) – The dimension along which to split. If \(dim < 0\), the -dimension to split along is \(rank(input) + dim\).
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • x – (Tensor) The input of clip_by_norm op.The number of dimensions must be between [1, 9]. +Duplicable: False Optional: False
      • +
    • max_norm (FLOAT) – (float) The maximum norm value.
    返回:

    The list of segmented tensor variables.

    -
    返回类型:

    List

    +
    返回:

    (Tensor) The output of clip_by_norm op with shape as input(X)
    -

    Examples

    -
    # x is a Tensor variable with shape [3, 9, 5]:
-x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
-x0.shape  # [3, 3, 5]
-x1.shape  # [3, 3, 5]
-x2.shape  # [3, 3, 5]
-x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
-x0.shape  # [3, 2, 5]
-x1.shape  # [3, 3, 5]
-x2.shape  # [3, 4, 5]
-
    -
    -
    -

    matmul

    +
    +

    sequence_softmax

    -paddle.v2.fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, name=None)
    -

    Applies matrix multiplication to two tensors.

    -

    Currently, the input tensors’ rank can be any, but when the rank of any -inputs is bigger than 3, this two inputs’ rank should be equal.

    -

    The actual behavior depends on the shapes of \(x\), \(y\) and the -flag values of transpose_x, transpose_y. Specifically:

    -
      -
    • If a transpose flag is specified, the last two dimensions of the tensor -are transposed. If the tensor is rank-1 of shape \([D]\), then for -\(x\) it is treated as \([1, D]\) in nontransposed form and as -\([D, 1]\) in transposed form, whereas for \(y\) it is the -opposite: It is treated as \([D, 1]\) in nontransposed form and as -\([1, D]\) in transposed form.
      • -
    • After transpose, the two tensors are 2-D or n-D and matrix multiplication -performs in the following way.
        -
      • If both are 2-D, they are multiplied like conventional matrices.
        • -
      • If either is n-D, it is treated as a stack of matrices residing in the -last two dimensions and a batched matrix multiply supporting broadcast -applies on the two tensors.
        • -
      -
      • -
    -

    Also note that if the raw tensor \(x\) or \(y\) is rank-1 and -nontransposed, the prepended or appended dimension \(1\) will be -removed after matrix multiplication.

    +paddle.v2.fluid.layers.sequence_softmax(**kwargs) +

    Sequence Softmax Operator.

    +

    SequenceSoftmaxOp computes the softmax activation among all time-steps for each +sequence. The dimension of each time-step should be 1. Thus, the shape of +input Tensor can be either [N, 1] or [N], where N is the sum of the length +of all sequences.

    +

    The algorithm works as follows:

    +
    +
    for i-th sequence in a mini-batch:
    +

    $$ +Out(X[lod[i]:lod[i+1]], :) = frac{exp(X[lod[i]:lod[i+1], :])} {sum(exp(X[lod[i]:lod[i+1], :]))} +$$

    +

    For example, for a mini-batch of 3 sequences with variable-length, +each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], +then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] +and N turns out to be 7.

    - - - + - +
    参数:
      -
    • x (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • y (Variable) – The input variable which is a Tensor or LoDTensor.
      • -
    • transpose_x (bool) – Whether to transpose \(x\) before multiplication.
      • -
    • transpose_y (bool) – Whether to transpose \(y\) before multiplication.
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • -
    -
    返回:

    The product Tensor variable.

    -
    参数:x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. +Duplicable: False Optional: False
    返回类型:

    Variable

    -
    返回:(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.
    -

    Examples

    -
    # Examples to clarify shapes of the inputs and output
-# x: [B, ..., M, K], y: [B, ..., K, N]
-fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
-
-# x: [B, M, K], y: [B, K, N]
-fluid.layers.matmul(x, y)  # out: [B, M, N]
-
-# x: [B, M, K], y: [K, N]
-fluid.layers.matmul(x, y)  # out: [B, M, N]
-
-# x: [M, K], y: [K, N]
-fluid.layers.matmul(x, y)  # out: [M, N]
-
-# x: [B, M, K], y: [K]
-fluid.layers.matmul(x, y)  # out: [B, M]
-
-# x: [K], y: [K]
-fluid.layers.matmul(x, y)  # out: [1]
+
    -# x: [M], y: [N] -fluid.layers.matmul(x, y, True, True) # out: [M, N] -
    +
    +

    sigmoid

    +
    +
    +paddle.v2.fluid.layers.sigmoid(**kwargs)
    +

    Sigmoid Activation Operator

    +

    $$out = frac{1}{1 + e^{-x}}$$

    + +++ + + + + + +
    参数:x – Input of Sigmoid operator +Duplicable: False Optional: False
    返回:Output of Sigmoid operator
    -

    logsigmoid

    +

    logsigmoid

    paddle.v2.fluid.layers.logsigmoid(**kwargs)
    @@ -3023,7 +3874,7 @@ Duplicable: False Optional: False
    -

    exp

    +

    exp

    paddle.v2.fluid.layers.exp(**kwargs)
    @@ -3044,7 +3895,7 @@ Duplicable: False Optional: False
    -

    relu

    +

    relu

    paddle.v2.fluid.layers.relu(**kwargs)
    @@ -3065,7 +3916,7 @@ Duplicable: False Optional: False
    -

    tanh

    +

    tanh

    paddle.v2.fluid.layers.tanh(**kwargs)
    @@ -3086,7 +3937,7 @@ Duplicable: False Optional: False
    -

    tanh_shrink

    +

    tanh_shrink

    paddle.v2.fluid.layers.tanh_shrink(**kwargs)
    @@ -3107,7 +3958,7 @@ Duplicable: False Optional: False
    -

    softshrink

    +

    softshrink

    paddle.v2.fluid.layers.softshrink(**kwargs)
    @@ -3140,7 +3991,7 @@ Duplicable: False Optional: False
    -

    sqrt

    +

    sqrt

    paddle.v2.fluid.layers.sqrt(**kwargs)
    @@ -3161,7 +4012,7 @@ Duplicable: False Optional: False
    -

    abs

    +

    abs

    paddle.v2.fluid.layers.abs(**kwargs)
    @@ -3182,7 +4033,7 @@ Duplicable: False Optional: False
    -

    ceil

    +

    ceil

    paddle.v2.fluid.layers.ceil(**kwargs)
    @@ -3203,7 +4054,7 @@ Duplicable: False Optional: False
    -

    floor

    +

    floor

    paddle.v2.fluid.layers.floor(**kwargs)
    @@ -3224,7 +4075,7 @@ Duplicable: False Optional: False
    -

    round

    +

    round

    paddle.v2.fluid.layers.round(**kwargs)
    @@ -3245,7 +4096,7 @@ Duplicable: False Optional: False
    -

    reciprocal

    +

    reciprocal

    paddle.v2.fluid.layers.reciprocal(**kwargs)
    @@ -3266,7 +4117,7 @@ Duplicable: False Optional: False
    -

    log

    +

    log

    paddle.v2.fluid.layers.log(**kwargs)
    @@ -3288,7 +4139,7 @@ Duplicable: False Optional: False
    -

    square

    +

    square

    paddle.v2.fluid.layers.square(**kwargs)
    @@ -3309,7 +4160,7 @@ Duplicable: False Optional: False
    -

    softplus

    +

    softplus

    paddle.v2.fluid.layers.softplus(**kwargs)
    @@ -3330,12 +4181,12 @@ Duplicable: False Optional: False
    -

    softsign

    +

    softsign

    paddle.v2.fluid.layers.softsign(**kwargs)

    Softsign Activation Operator.

    -

    $$out = frac{x}{1 + |x|}$$

    +

    $$out = frac{x}{1 + |x|}$$

    @@ -3351,7 +4202,7 @@ Duplicable: False Optional: False
    -

    brelu

    +

    brelu

    paddle.v2.fluid.layers.brelu(**kwargs)
    @@ -3378,7 +4229,7 @@ Duplicable: False Optional: False
    -

    leaky_relu

    +

    leaky_relu

    paddle.v2.fluid.layers.leaky_relu(**kwargs)
    @@ -3404,7 +4255,7 @@ Duplicable: False Optional: False
    -

    soft_relu

    +

    soft_relu

    paddle.v2.fluid.layers.soft_relu(**kwargs)
    @@ -3430,7 +4281,7 @@ Duplicable: False Optional: False
    -

    elu

    +

    elu

    paddle.v2.fluid.layers.elu(**kwargs)
    @@ -3458,7 +4309,7 @@ Duplicable: False Optional: False
    -

    relu6

    +

    relu6

    paddle.v2.fluid.layers.relu6(**kwargs)
    @@ -3484,7 +4335,7 @@ Duplicable: False Optional: False
    -

    pow

    +

    pow

    paddle.v2.fluid.layers.pow(**kwargs)
    @@ -3508,9 +4359,36 @@ Duplicable: False Optional: False
    +
    +
    +

    stanh

    +
    +
    +paddle.v2.fluid.layers.stanh(**kwargs)
    +

    STanh Activation Operator.

    +

    $$out = b * frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$

    + +++ + + + + + +
    参数:
      +
    • x – Input of STanh operator +Duplicable: False Optional: False
      • +
    • scale_a (FLOAT) – The scale parameter of a for the input
      • +
    • scale_b (FLOAT) – The scale parameter of b for the input
      • +
    +
    返回:

    Output of STanh operator

    +
    +
    +
    -

    hard_shrink

    +

    hard_shrink

    paddle.v2.fluid.layers.hard_shrink(**kwargs)
    @@ -3543,7 +4421,7 @@ Duplicable: False Optional: False
    -

    thresholded_relu

    +

    thresholded_relu

    paddle.v2.fluid.layers.thresholded_relu(**kwargs)
    @@ -3575,7 +4453,7 @@ Duplicable: False Optional: False
    -

    hard_sigmoid

    +

    hard_sigmoid

    paddle.v2.fluid.layers.hard_sigmoid(**kwargs)
    @@ -3607,7 +4485,7 @@ Duplicable: False Optional: False
    -

    swish

    +

    swish

    paddle.v2.fluid.layers.swish(**kwargs)
    @@ -3632,169 +4510,143 @@ Duplicable: False Optional: False
    -
    -

    im2sequence

    +
    +
    +

    tensor

    +
    +

    create_tensor

    -paddle.v2.fluid.layers.im2sequence(input, filter_size=1, stride=1, padding=0, name=None)
    -

    Extracts image patches from the input tensor to form a tensor of shape -{input.batch_size * output_height * output_width, filter_size_H * -filter_size_W * input.channels} which is similar with im2col. -This op use filter / kernel to scan images and convert these images to -sequences. After expanding, the number of time step are -output_height * output_width for an image, in which output_height and -output_width are calculated by below equation:

    -
    -\[output\_size = 1 + (2 * padding + img\_size - block\_size + stride - 1) / stride\]
    -

    And the dimension of each time step is block_y * block_x * input.channels.

    +paddle.v2.fluid.layers.create_tensor(dtype, name=None) +
    + +
    +
    +

    create_parameter

    +
    +
    +paddle.v2.fluid.layers.create_parameter(shape, dtype, attr=None, is_bias=False, default_initializer=None)
    +

    Create a parameter +:param shape: shape of the parameter +:type shape: list[int] +:param dtype: element type of the parameter +:type dtype: string +:param attr: attributes of the parameter +:type attr: ParamAttr +:param is_bias: This can affect which default initializer is chosen

    +
    +
    when default_initializer is None. If is_bias, +initializer.Constant(0.0) will be used. Otherwise, +Xavier() will be used.
    - + - + - +
    参数:
      -
    • input (Variable) – The input should be a tensor in NCHW format.
      • -
    • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, -it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square.
      • -
    • stride (int|tuple) – The stride size. If stride is a tuple, it must -contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride. Default: stride = 1.
      • -
    • padding (int|tuple) – The padding size. If padding is a tuple, it can -contain two integers like (padding_H, padding_W) which means -padding_up = padding_down = padding_H and -padding_left = padding_right = padding_W. Or it can use -(padding_up, padding_left, padding_down, padding_right) to indicate -paddings of four direction. Otherwise, a scalar padding means -padding_up = padding_down = padding_left = padding_right = padding -Default: padding = 0.
      • -
    • name (int) – The name of this layer. It is optional.
      • -
    -
    参数:default_initializer (Initializer) – initializer for the parameter
    返回:

    The output is a LoDTensor with shape -{input.batch_size * output_height * output_width, -filter_size_H * filter_size_W * input.channels}. -If we regard output as a matrix, each row of this matrix is -a step of a sequence.

    -
    返回:the created parameter
    返回类型:

    output

    -
    返回类型:Parameter
    -

    Examples:

    -

    As an example:

    -
    -
    Given:
-
-x = [[[[ 6.  2.  1.]
-       [ 8.  3.  5.]
-       [ 0.  2.  6.]]
-
-      [[ 2.  4.  4.]
-       [ 6.  3.  0.]
-       [ 6.  4.  7.]]]
-
-     [[[ 6.  7.  1.]
-       [ 5.  7.  9.]
-       [ 2.  4.  8.]]
-
-      [[ 1.  2.  1.]
-       [ 1.  3.  5.]
-       [ 9.  0.  8.]]]]
-
-x.dims = {2, 2, 3, 3}
-
-And:
-
-filter = [2, 2]
-stride = [1, 1]
-padding = [0, 0]
-
-Then:
-
-output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
-               [ 2.  1.  3.  5.  4.  4.  3.  0.]
-               [ 8.  3.  0.  2.  6.  3.  6.  4.]
-               [ 3.  5.  2.  6.  3.  0.  4.  7.]
-               [ 6.  7.  5.  7.  1.  2.  1.  3.]
-               [ 7.  1.  7.  9.  2.  1.  3.  5.]
-               [ 5.  7.  2.  4.  1.  3.  9.  0.]
-               [ 7.  9.  4.  8.  3.  5.  0.  8.]]
+
    -output.dims = {8, 9} +
    +
    +

    create_global_var

    +
    +
    +paddle.v2.fluid.layers.create_global_var(shape, value, dtype, persistable=False, name=None)
    +
    -output.lod = [[0, 4, 8]] -
    -

    The simple usage is:

    -
    output = fluid.layers.im2sequence(
-    input=layer, stride=[1, 1], filter_size=[2, 2])
-
    +
    +

    cast

    +
    +
    +paddle.v2.fluid.layers.cast(x, dtype)
    +

    This function takes in the input with input_dtype +and casts it to the output_dtype as the output.

    +
    +
    -
    +
    +

    concat

    +
    +
    +paddle.v2.fluid.layers.concat(input, axis=0)
    +

    Concat

    +

    This function concatenates the input along the axis mentioned +and returns that as the output.

    + +++ + + + + + + + +
    参数:
      +
    • input (list) – List of tensors to be concatenated
      • +
    • axis (int) – Integer axis along which the tensors will be concatenated
      • +
    +
    返回:

    Output variable of the concatenation

    +
    返回类型:

    Variable

    +
    +

    Examples

    -
    -

    edit_distance

    -
    -
    -

    ctc_greedy_decoder

    +
    +

    sums

    -paddle.v2.fluid.layers.ctc_greedy_decoder(input, blank, name=None)
    -

    This op is used to decode sequences by greedy policy by below steps: -1. Get the indexes of max value for each row in input. a.k.a.

    -
    -
    numpy.argmax(input, axis=0).
    -
      -
    1. For each sequence in result of step1, merge repeated tokens between two -blanks and delete all blanks.
      2. -
    -

    A simple example as below:

    -
    Given:
-
-input.data = [[0.6, 0.1, 0.3, 0.1],
-              [0.3, 0.2, 0.4, 0.1],
-              [0.1, 0.5, 0.1, 0.3],
-              [0.5, 0.1, 0.3, 0.1],
-
-              [0.5, 0.1, 0.3, 0.1],
-              [0.2, 0.2, 0.2, 0.4],
-              [0.2, 0.2, 0.1, 0.5],
-              [0.5, 0.1, 0.3, 0.1]]
-
-input.lod = [[0, 4, 8]]
-
-Then:
-
-output.data = [[2],
-               [1],
-               [3]]
+paddle.v2.fluid.layers.sums(input, out=None)
+
    This function performs the sum operation on the input and returns the
+result as the output.
    
+
+++
+
+
+
+
+
+
+
+
    


    参数:input (Variable|list) – The input tensor that has the elements
+that need to be summed up.
    返回:
    
+
    The tensor type variable that has the sum of input
    
+
    written to it.
    
+
    
+
    返回类型:Variable
    
+
    Examples
    
+
    -output.lod = [[0, 2, 3]] -
    +
    +

    assign

    +
    +
    +paddle.v2.fluid.layers.assign(input, output)
    +

    Assign

    +

    This function copies the input Variable to the output Variable.

    -
    参数:
      -
    • input (Variable) – (LoDTensor<float>), the probabilities of -variable-length sequences, which is a 2-D Tensor with -LoD information. It’s shape is [Lp, num_classes + 1], -where Lp is the sum of all input sequences’ length and -num_classes is the true number of classes. (not -including the blank label).
      • -
    • blank (int) – the blank label index of Connectionist Temporal -Classification (CTC) loss, which is in thehalf-opened -interval [0, num_classes + 1).
      • +
    • input (Variable|numpy.ndarray) – The source variable
      • +
    • output (Variable) – The destination variable
    返回:

    CTC greedy decode result.

    +
    返回:

    The destination variable that was supplied as the output.
    返回类型:

    Variable

    @@ -3803,41 +4655,34 @@ interval [0, num_classes + 1).

    Examples

    -
    x = fluid.layers.data(name='x', shape=[8], dtype='float32')
-
-cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
-
    -
    -
    -

    l2_normalize

    +
    +

    fill_constant_batch_size_like

    -paddle.v2.fluid.layers.l2_normalize(x, axis, epsilon=1e-12, name=None)
    -

    L2 normalize Layer

    -

    The l2 normalize layer normalizes x along dimension axis using an L2 -norm. For a 1-D tensor (dim is fixed to 0), this layer computes

    -

    output = x / sqrt(max(sum(x**2), epsilon))

    -

    For x with more dimensions, this layer independently normalizes each 1-D -slice along dimension axis.

    +paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0) +

    fill_constant_batch_size_like

    +

    This function creates a tensor of specified shape, dtype and batch size, +and initializes this with a constant supplied in value. The batch size is +obtained from the input tensor.

    +

    It also sets stop_gradient to True.

    -
    参数:
      -
    • x (Variable|list) – The input tensor to l2_normalize layer.
      • -
    • axis (int) – Dimension along which to normalize the input.
      • -
    • epsilon (float) – A lower bound value for x‘s l2 norm. sqrt(epsilon) will -be used as the divisor if the l2 norm of x is less than -sqrt(epsilon).
      • -
    • name (str|None) – A name for this layer(optional). If set None, the layer -will be named automatically.
      • +
    • input (Variable) – Tensor whose dimensions will be used to get batch size
      • +
    • shape (tuple|list|None) – Shape of output tensor
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
      • +
    • value (float) – Constant value to initialize the output tensor
      • +
    • input_dim_idx (int) – Index of input’s batch size dimension
      • +
    • output_dim_idx (int) – Index of output’s batch size dimension
    返回:

    The output tensor variable.

    +
    返回:

    The tensor variable storing the output
    返回类型:

    Variable

    @@ -3846,55 +4691,35 @@ will be named automatically.

    Examples

    -
    data = fluid.layers.data(name="data",
-                         shape=(3, 17, 13),
-                         dtype="float32")
-normed = fluid.layers.l2_normalize(x=data, axis=1)
+
    data = fluid.layers.fill_constant_batch_size_like(
+    input=like, shape=[1], value=0, dtype='int64')
    -
    -

    sequence_reshape

    +
    +

    fill_constant

    -paddle.v2.fluid.layers.sequence_reshape(input, new_dim)
    -

    Sequence Reshape Layer

    -

    This layer will rearrange the input sequences. The new dimension is set by -user. Length of each sequence is computed according to original length, -original dimension and new dimension. The following example will help to -illustrate the function of this layer:

    -
    x is a LoDTensor:
-    x.lod  = [[0, 2, 6]]
-    x.data = [[1, 2], [3, 4],
-              [5, 6], [7, 8], [9, 10], [11, 12]]
-    x.dims = [6, 2]
-
-set new_dim = 4
-
-then out is a LoDTensor:
-    out.lod  = [[0, 1, 3]]
-    out.data = [[1, 2, 3, 4],
-                [5, 6, 7, 8], [9, 10, 11, 12]]
-    out.dims = [3, 4]
-
    -
    -

    Currently, only 1-level LoDTensor is supported and please make sure -(original length * original dimension) can be divided by new dimension with -no remainder for each sequence.

    +paddle.v2.fluid.layers.fill_constant(shape, dtype, value, force_cpu=False, out=None) +

    fill_constant

    +

    This function creates a tensor with specified shape and dtype, and +initializes it with a constant specifed by value.

    +

    The attribute stop_gradient of the created tensor is set to True.

    -
    参数:
      -
    • input (Variable) – (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor -with shape being [N, M] where M for dimension.
      • -
    • new_dim (int) – New dimension which the input LoDTensor is reshaped to.
      • +
    • shape (tuple|list|None) – Shape of the output tensor.
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of the output tensor.
      • +
    • value (float) – The constant value used to initialize the output tensor.
      • +
    • out (Variable) – The output tensor.
    返回:

    Reshaped LoDTensor according to new dimension.

    +
    返回:

    The tensor variable storing the output.
    返回类型:

    Variable

    @@ -3903,49 +4728,32 @@ with shape being [N, M] where M for dimension.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[5, 20],
-                  dtype='float32', lod_level=1)
-x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
+
    data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
    -
    -

    row_conv

    +
    +

    ones

    -paddle.v2.fluid.layers.row_conv(input, future_context_size, param_attr=None, act=None)
    -

    Row Conv Operator. This layer will apply lookahead convolution to -input. The input variable should be a 2D LoDTensor with shape [T, D]. -Parameters with shape [future_context_size + 1, D] will be created. The math -equation of row convolution is as follows:

    -
    -\[Out_{i} = \sum_{j = i} ^ {i + \tau} X_{j} \odot W_{i - j}\]
    -

    In the above equation:

    -
      -
    • \(Out_{i}\): The i-th row of output variable with shape [1, D].
      • -
    • \(\tau\): Future context size.
      • -
    • \(X_{j}\): The j-th row of input variable with shape [1, D].
      • -
    • \(W_{i-j}\): The (i-j)-th row of parameters with shape [1, D].
      • -
    -

    More details about row_conv please refer to the paper (http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and -the design document (https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).

    +paddle.v2.fluid.layers.ones(shape, dtype) +

    ones

    +

    This function creates a tensor of specified shape and +dtype, and initializes this with 1.

    +

    It also sets stop_gradient to True.

    -
    参数:
      -
    • input (Variable) – Input variable, a 2D LoDTensor with shape [T, D].
      • -
    • future_context_size (int) – Future context size. Please note, the shape -of convolution kernel is [future_context_size + 1, D].
      • -
    • param_attr (ParamAttr) – Attributes of parameters, including -name, initializer etc.
      • -
    • act (str) – Non-linear activation to be applied to output variable.
      • +
    • shape (tuple|list|None) – Shape of output tensor
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    返回:

    The output tensor with same shape as input tensor.

    +
    返回:

    The tensor variable storing the output
    返回类型:

    Variable

    @@ -3954,48 +4762,32 @@ name, initializer etc.

    Examples

    -
    x = fluid.layers.data(name='x', shape=[16],
-                dtype='float32', lod_level=1)
-out = fluid.layers.row_conv(input=x, future_context_size=2)
+
    data = fluid.layers.ones(shape=[1], dtype='int64')
    -
    -

    multiplex

    +
    +

    zeros

    -paddle.v2.fluid.layers.multiplex(inputs, index)
    -

    Multiplex Layer

    -

    Referring to the given index variable, this layer selects rows from the -input variables to construct a multiplex variable. Assuming that there are -\(m\) input variables and \(I_i\) represents the i-th input -variable and \(i\) is in [0, \(m\)). All input variables are -tensors with same shape [\(d_0\), \(d_1\), ..., \(d_R\)]. -Please note that rank of the input tensor should be at least 2. Each input -variable will be treated as a 2-D matrix with shape [\(M\), \(N\)] -where \(M\) for \(d_0\) and \(N\) for \(d_1\) * \(d_2\) -* ... * \(d_R\). Let \(I_i[j]\) be the j-th row of the i-th input -variable. The given index variable should be a 2-D tensor with shape -[\(M\), 1]. Let ID[i] be the i-th index value of the index variable. -Then the output variable will be a tensor with shape [\(d_0\), -\(d_1\), ..., \(d_R\)]. If we treat the output tensor as a 2-D -matrix with shape [\(M\), \(N\)] and let \(O[i]\) be the i-th -row of the matrix, then O[i] is equal to \(I_{ID[i]}[i]\).

    +paddle.v2.fluid.layers.zeros(shape, dtype) +

    zeros

    +

    This function creates a tensor of specified shape and +dtype, and initializes this with 0.

    +

    It also sets stop_gradient to True.

    -
    参数:
      -
    • inputs (list) – A list of variables to gather from. All variables have the -same shape and the rank is at least 2.
      • -
    • index (Variable) – Tensor<int32>, index variable which is a 2-D tensor -with shape [M, 1] where M is the batch size.
      • +
    • shape (tuple|list|None) – Shape of output tensor
      • +
    • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    返回:

    Multiplex variable gathered from input variables.

    +
    返回:

    The tensor variable storing the output
    返回类型:

    Variable

    @@ -4004,14 +4796,12 @@ with shape [M, 1] where M is the batch size.

    Examples

    -
    x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
-x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
-index = fluid.layers.data(name='index', shape=[1], dtype='int32')
-out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
+
    data = fluid.layers.zeros(shape=[1], dtype='int64')
    +
    @@ -4022,7 +4812,7 @@ with shape [M, 1] where M is the batch size. @@ -236,7 +236,7 @@
    -

    Nets

    +

    nets

    simple_img_conv_pool

    @@ -244,15 +244,6 @@ paddle.v2.fluid.nets.simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, param_attr=None, pool_type='max', use_cudnn=True)
    -
    -
    -

    img_conv_group

    -
    -
    -paddle.v2.fluid.nets.img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, param_attr=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=0.0, pool_stride=1, pool_type=None, use_cudnn=True)
    -

    Image Convolution Group, Used for vgg net.

    -
    -

    sequence_conv_pool

    @@ -380,10 +371,10 @@ parameters.

    diff --git a/develop/doc_cn/api/v2/fluid/optimizer.html b/develop/doc_cn/api/v2/fluid/optimizer.html index f3d46b5af6..14cc626456 100644 --- a/develop/doc_cn/api/v2/fluid/optimizer.html +++ b/develop/doc_cn/api/v2/fluid/optimizer.html @@ -8,7 +8,7 @@ - Optimizer — PaddlePaddle 文档 + optimizer — PaddlePaddle 文档 @@ -34,8 +34,8 @@ - - + + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • Optimizer
    • +
  • optimizer
    • @@ -236,113 +236,58 @@
    -

    Optimizer

    -
    -

    Optimizer

    -
    -
    -class paddle.v2.fluid.optimizer.Optimizer(learning_rate, global_step=None, regularization=None)
    -

    Optimizer Base class.

    -

    Define the common interface of an optimizer. -User should not use this class directly, -but need to use one of it’s implementation.

    +

    optimizer

    +
    +

    SGD

    -global_learning_rate
    -

    get global decayed learning rate -:return:

    -
    - -
    -
    -create_optimization_pass(parameters_and_grads, loss, startup_program=None)
    -

    Add optimization operators to update gradients to variables.

    - --- - - - - - - - -
    参数:
      -
    • loss – the target that this optimization is for.
      • -
    • parameters_and_grads – a list of (variable, gradient) pair to update.
      • -
    -
    返回:

    a list of operators that will complete one step of -optimization. This will include parameter update ops, global step -update ops and any other custom ops required by subclasses to manage -their internal state. -:param startup_program:

    -
    返回类型:

    return_op_list

    -
    -
    - -
    -
    -minimize(loss, startup_program=None, parameter_list=None, no_grad_set=None)
    -

    Add operations to minimize loss by updating parameter_list.

    -

    This method combines interface append_backward() and -create_optimization_pass() into one.

    -
    - +paddle.v2.fluid.optimizer.SGD +

    SGDOptimizer 的别名

    -
    -

    SGDOptimizer

    -
    -
    -class paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, **kwargs)
    -

    Simple SGD optimizer without any state.

    -
    - -
    -
    -

    MomentumOptimizer

    -
    +
    +

    Momentum

    +
    -class paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, **kwargs)
    -

    Simple Momentum optimizer with velocity state

    +paddle.v2.fluid.optimizer.Momentum +

    MomentumOptimizer 的别名

    -
    -

    AdagradOptimizer

    -
    +
    +

    Adagrad

    +
    -class paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, **kwargs)
    -

    Simple Adagrad optimizer with moment state

    +paddle.v2.fluid.optimizer.Adagrad +

    AdagradOptimizer 的别名

    -
    -

    AdamOptimizer

    -
    +
    +

    Adam

    +
    -class paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
    -

    Implements the Adam Optimizer

    +paddle.v2.fluid.optimizer.Adam +

    AdamOptimizer 的别名

    -
    -

    AdamaxOptimizer

    -
    +
    +

    Adamax

    +
    -class paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
    -

    Implements the Adamax Optimizer

    +paddle.v2.fluid.optimizer.Adamax +

    AdamaxOptimizer 的别名

    -
    -

    DecayedAdagradOptimizer

    -
    +
    +

    DecayedAdagrad

    +
    -class paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, **kwargs)
    -

    Simple Decayed Adagrad optimizer with moment state

    +paddle.v2.fluid.optimizer.DecayedAdagrad +

    DecayedAdagradOptimizer 的别名

    @@ -355,10 +300,10 @@ their internal state. diff --git a/develop/doc_cn/api/v2/fluid/param_attr.html b/develop/doc_cn/api/v2/fluid/param_attr.html index 95cd8721cf..b6a04e9256 100644 --- a/develop/doc_cn/api/v2/fluid/param_attr.html +++ b/develop/doc_cn/api/v2/fluid/param_attr.html @@ -8,7 +8,7 @@ - ParamAttr — PaddlePaddle 文档 + param_attr — PaddlePaddle 文档 @@ -34,8 +34,8 @@ - - + + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • ParamAttr
    • +
  • param_attr
    • @@ -235,10 +235,26 @@
    -
    -

    ParamAttr

    -
    -

    ParamAttr

    +
    +

    param_attr

    +
    +

    ParamAttr

    +
    +
    +class paddle.v2.fluid.param_attr.ParamAttr(name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, gradient_clip=None)
    +
    + +
    +
    +

    WeightNormParamAttr

    +
    +
    +class paddle.v2.fluid.param_attr.WeightNormParamAttr(dim=None, **kwargs)
    +

    Used for weight normalization. Any field in ParamAttr can also be set here. +Besides, an extra field dim can be set to indicate the dimension except +which to normalize.

    +
    +
    @@ -249,10 +265,10 @@ diff --git a/develop/doc_cn/api/v2/fluid/profiler.html b/develop/doc_cn/api/v2/fluid/profiler.html index 88a35e02d0..9a40198462 100644 --- a/develop/doc_cn/api/v2/fluid/profiler.html +++ b/develop/doc_cn/api/v2/fluid/profiler.html @@ -8,7 +8,7 @@ - Profiler — PaddlePaddle 文档 + profiler — PaddlePaddle 文档 @@ -34,8 +34,8 @@ - - + + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • Profiler
    • +
  • profiler
    • @@ -236,9 +236,9 @@
    -

    Profiler

    -
    -

    Profiler

    +

    profiler

    +
    +

    cuda_profiler

    paddle.v2.fluid.profiler.cuda_profiler(*args, **kwds)
    @@ -268,6 +268,53 @@ to “Compute Command Line Profiler User Guide”.
    +
    +
    +

    reset_profiler

    +
    +
    +paddle.v2.fluid.profiler.reset_profiler()
    +

    The profiler clear interface. +reset_profiler will clear the previous time record.

    +
    + +
    +
    +

    profiler

    +
    +
    +paddle.v2.fluid.profiler.profiler(*args, **kwds)
    +

    The profiler interface. +Different from cuda_profiler, this profiler can be used to profile both CPU +and GPU program. By defalut, it records the CPU and GPU operator kernels, +if you want to profile other program, you can refer the profiling tutorial +to add more records.

    + +++ + + + +
    参数:
      +
    • state (string) – The profiling state, which should be ‘CPU’ or ‘GPU’, +telling the profiler to use CPU timer or GPU timer for profiling. +Although users may have already specified the execution place +(CPUPlace/CUDAPlace) in the begining, for flexibility the profiler +would not inherit this place.
      • +
    • sorted_key (string) – If None, the profiling results will be printed +in the order of first end time of events. Otherwise, the profiling +results will be sorted by the this flag. This flag should be one +of ‘calls’, ‘total’, ‘max’, ‘min’ or ‘ave’. +The calls means sorting by the number of calls. +The total means sorting by the total execution time. +The max means sorting by the maximum execution time. +The min means sorting by the minimum execution time. +The ave means sorting by the average execution time.
      • +
    +
    +
    +
    @@ -278,10 +325,10 @@ to “Compute Command Line Profiler User Guide”. diff --git a/develop/doc_cn/api/v2/fluid/regularizer.html b/develop/doc_cn/api/v2/fluid/regularizer.html index e88ceb3a4a..67cb333c7c 100644 --- a/develop/doc_cn/api/v2/fluid/regularizer.html +++ b/develop/doc_cn/api/v2/fluid/regularizer.html @@ -8,7 +8,7 @@ - Regularizer — PaddlePaddle 文档 + regularizer — PaddlePaddle 文档 @@ -34,8 +34,8 @@ - - + + @@ -173,17 +173,17 @@
  • 训练与应用
  • Fluid
    • @@ -226,7 +226,7 @@
  • Fluid >
    • -
  • Regularizer
    • +
  • regularizer
    • @@ -236,37 +236,55 @@
    -

    Regularizer

    -
    -

    WeightDecayRegularizer

    -
    +

    regularizer

    +
    +

    append_regularization_ops

    +
    -class paddle.v2.fluid.regularizer.WeightDecayRegularizer
    -

    Base class for weight decay regularizers

    -

    Defines the common interface of weight-decay regularizers. -Weight-decay regularizers are added only during the backward -pass for faster regularization. They add operations to the network -that correspond to gradient of the regularization function. -Users should not use this class directly, but need to use one -of its implementations

    +paddle.v2.fluid.regularizer.append_regularization_ops(parameters_and_grads, regularization=None) +

    Create and add backward regularization Operators

    +

    Creates and adds backward regularization operators in the BlockDesc. +This will add gradients of the regularizer function to the gradients +of the parameters and return these modified gradients. This is the +same as implementing weight decay in optimizers for regularization.

    + +++ + + + + + + + +
    参数:
      +
    • parameters_and_grads – A list of (parameters, gradients) pairs +that need to be regularized.
      • +
    • regularization – A global regularizer. If the parameter is not +set. It will be applied with regularizer.
      • +
    +
    返回:

    list of (parameters, gradients) pair with the regularized gradient

    +
    Raises:

    Exception – Unknown regularization type

    +
    -
    -

    L2DecayRegularizer

    -
    +
    +

    L1Decay

    +
    -class paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
    -

    Implements the L2 Weight Decay Regularization

    +paddle.v2.fluid.regularizer.L1Decay +

    L1DecayRegularizer 的别名

    -
    -

    L1DecayRegularizer

    -
    -
    -class paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)
    -

    Implements the L1 Weight Decay Regularization

    +
    +

    L2Decay

    +
    +
    +paddle.v2.fluid.regularizer.L2Decay
    +

    L2DecayRegularizer 的别名

    @@ -279,10 +297,10 @@ of its implementations

    diff --git a/develop/doc_cn/api/v2/model_configs.html b/develop/doc_cn/api/v2/model_configs.html index 1015d2b571..b3098284e7 100644 --- a/develop/doc_cn/api/v2/model_configs.html +++ b/develop/doc_cn/api/v2/model_configs.html @@ -173,17 +173,17 @@
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  • 训练与应用
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  • 训练与应用
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  • 训练与应用
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  • 训练与应用
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  • 训练与应用
  • Fluid
    • @@ -248,11 +248,6 @@     		paddle
        - paddle.v2.fluid.regularizer -
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  • 训练与应用
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  • 训练与应用
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