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