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107 lines
3.8 KiB
107 lines
3.8 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_subseq')
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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 simple RNN in
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# sequence_rnn_multi_unequalength_inputs.conf
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def outer_step(x1, x2):
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outer_mem1 = memory(name = "outer_rnn_state1", size = hidden_dim)
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outer_mem2 = memory(name = "outer_rnn_state2", size = hidden_dim)
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def inner_step1(y):
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inner_mem = memory(name = 'inner_rnn_state_' + y.name,
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size = hidden_dim,
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boot_layer = outer_mem1)
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out = fc_layer(input = [y, inner_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 = 'inner_rnn_state_' + y.name)
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return out
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def inner_step2(y):
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inner_mem = memory(name = 'inner_rnn_state_' + y.name,
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size = hidden_dim,
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boot_layer = outer_mem2)
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out = fc_layer(input = [y, inner_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 = 'inner_rnn_state_' + y.name)
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return out
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encoder1 = recurrent_group(
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step = inner_step1,
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name = 'inner1',
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input = x1)
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encoder2 = recurrent_group(
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step = inner_step2,
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name = 'inner2',
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input = x2)
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sentence_last_state1 = last_seq(input = encoder1, name = 'outer_rnn_state1')
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sentence_last_state2_ = last_seq(input = encoder2, name = 'outer_rnn_state2')
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encoder1_expand = expand_layer(input = sentence_last_state1,
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expand_as = encoder2)
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return [encoder1_expand, encoder2]
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encoder1_rep, encoder2_rep = recurrent_group(
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name="outer",
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step=outer_step,
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input=[SubsequenceInput(emb1), SubsequenceInput(emb2)],
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targetInlink=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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