#edit-mode: -*- python -*- # Copyright (c) 2016 Baidu, Inc. All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from paddle.trainer_config_helpers import * ######################## data source ################################ define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list', test_list=None, module='rnn_data_provider', obj='process_unequalength_subseq') settings(batch_size=2, learning_rate=0.01) ######################## network configure ################################ dict_dim = 10 word_dim = 8 hidden_dim = 8 label_dim = 2 speaker1 = data_layer(name="word1", size=dict_dim) speaker2 = data_layer(name="word2", size=dict_dim) emb1 = embedding_layer(input=speaker1, size=word_dim) emb2 = embedding_layer(input=speaker2, size=word_dim) # This hierachical RNN is designed to be equivalent to the simple RNN in # sequence_rnn_multi_unequalength_inputs.conf def outer_step(x1, x2): outer_mem1 = memory(name = "outer_rnn_state1", size = hidden_dim) outer_mem2 = memory(name = "outer_rnn_state2", size = hidden_dim) def inner_step1(y): inner_mem = memory(name = 'inner_rnn_state_' + y.name, size = hidden_dim, boot_layer = outer_mem1) out = fc_layer(input = [y, inner_mem], size = hidden_dim, act = TanhActivation(), bias_attr = True, name = 'inner_rnn_state_' + y.name) return out def inner_step2(y): inner_mem = memory(name = 'inner_rnn_state_' + y.name, size = hidden_dim, boot_layer = outer_mem2) out = fc_layer(input = [y, inner_mem], size = hidden_dim, act = TanhActivation(), bias_attr = True, name = 'inner_rnn_state_' + y.name) return out encoder1 = recurrent_group( step = inner_step1, name = 'inner1', input = x1) encoder2 = recurrent_group( step = inner_step2, name = 'inner2', input = x2) sentence_last_state1 = last_seq(input = encoder1, name = 'outer_rnn_state1') sentence_last_state2_ = last_seq(input = encoder2, name = 'outer_rnn_state2') encoder1_expand = expand_layer(input = sentence_last_state1, expand_as = encoder2) return [encoder1_expand, encoder2] encoder1_rep, encoder2_rep = recurrent_group( name="outer", step=outer_step, input=[SubsequenceInput(emb1), SubsequenceInput(emb2)], targetInlink=emb2) encoder1_last = last_seq(input = encoder1_rep) encoder1_expandlast = expand_layer(input = encoder1_last, expand_as = encoder2_rep) context = mixed_layer(input = [identity_projection(encoder1_expandlast), identity_projection(encoder2_rep)], size = hidden_dim) rep = last_seq(input=context) prob = fc_layer(size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True) outputs(classification_cost(input=prob, label=data_layer(name="label", size=label_dim)))