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86 lines
2.8 KiB
86 lines
2.8 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_mixed')
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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 = 2
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hidden_dim = 2
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label_dim = 2
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data1 = data_layer(name="word1", size=dict_dim)
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data2 = data_layer(name="word2", size=dict_dim)
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label = data_layer(name="label", size=label_dim)
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encoding = embedding_layer(input=data2, size=word_dim)
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subseq = embedding_layer(input=data1, size=word_dim)
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seq = embedding_layer(input=data2, size=word_dim)
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nonseq = embedding_layer(input=label, 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(subseq, seq, nonseq, encoding):
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outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
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def inner_step(subseq, seq, nonseq):
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inner_mem = memory(
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name="inner_rnn_state", size=hidden_dim, boot_layer=outer_mem)
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out = fc_layer(
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input=[subseq, seq, nonseq, 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')
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return out
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decoder = recurrent_group(
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step=inner_step, name='inner', input=[subseq, seq, nonseq])
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last = last_seq(name="outer_rnn_state", input=decoder)
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context = simple_attention(
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encoded_sequence=encoding, encoded_proj=encoding, decoder_state=last)
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return context
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out = recurrent_group(
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name="outer",
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step=outer_step,
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input=[
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subseq, expand_layer(
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seq, expand_as=subseq,
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expand_level=ExpandLevel.FROM_SEQUENCE), expand_layer(
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nonseq,
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expand_as=subseq,
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expand_level=ExpandLevel.FROM_NO_SEQUENCE),
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StaticInput(encoding)
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])
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rep = last_seq(input=out)
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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(classification_cost(input=prob, label=label))
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