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99 lines
3.1 KiB
99 lines
3.1 KiB
# edit-mode: -*- python -*-
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# Copyright (c) 2016 PaddlePaddle Authors. 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(
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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 hierarchical 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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index = [0]
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def inner_step(ipt):
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index[0] += 1
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i = index[0]
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outer_mem = memory(name="outer_rnn_state_%d" % i, size=hidden_dim)
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def inner_step_impl(y):
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inner_mem = memory(
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name="inner_rnn_state_" + y.name,
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size=hidden_dim,
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boot_layer=outer_mem)
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out = fc_layer(
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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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encoder = recurrent_group(
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step=inner_step_impl, name='inner_%d' % i, input=ipt)
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last = last_seq(name="outer_rnn_state_%d" % i, input=encoder)
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return encoder, last
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encoder1, sentence_last_state1 = inner_step(ipt=x1)
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encoder2, sentence_last_state2 = inner_step(ipt=x2)
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encoder1_expand = expand_layer(
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input=sentence_last_state1, 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, expand_as=encoder2_rep)
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context = mixed_layer(
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input=[
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identity_projection(encoder1_expandlast),
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identity_projection(encoder2_rep)
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],
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size=hidden_dim)
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rep = last_seq(input=context)
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prob = fc_layer(
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size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
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outputs(
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classification_cost(
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input=prob, label=data_layer(
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name="label", size=label_dim)))
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