#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_seq') settings(batch_size=2, learning_rate=0.01) ######################## network configure ################################ dict_dim = 10 word_dim = 8 hidden_dim = 8 label_dim = 3 data = data_layer(name="word", size=dict_dim) emb = embedding_layer(input=data, size=word_dim) def step(y): mem = memory(name="rnn_state", size=hidden_dim) return fc_layer(input=[y, mem], size=hidden_dim, act=TanhActivation(), bias_attr=True, name="rnn_state") out = recurrent_group( step=step, input=emb) value_printer_evaluator(input=out) rep = last_seq(input=out) 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)))