text: #  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)))
  