# edit-mode: -*- python -*- # Copyright (c) 2016 PaddlePaddle Authors. 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 hierarchical RNN is designed to be equivalent to the simple RNN in # sequence_rnn_multi_unequalength_inputs.conf def outer_step(x1, x2): index = [0] def inner_step(ipt): index[0] += 1 i = index[0] outer_mem = memory(name="outer_rnn_state_%d" % i, size=hidden_dim) def inner_step_impl(y): inner_mem = memory( name="inner_rnn_state_" + y.name, size=hidden_dim, boot_layer=outer_mem) out = fc_layer( input=[y, inner_mem], size=hidden_dim, act=TanhActivation(), bias_attr=True, name='inner_rnn_state_' + y.name) return out encoder = recurrent_group( step=inner_step_impl, name='inner_%d' % i, input=ipt) last = last_seq(name="outer_rnn_state_%d" % i, input=encoder) return encoder, last encoder1, sentence_last_state1 = inner_step(ipt=x1) encoder2, sentence_last_state2 = inner_step(ipt=x2) 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)))