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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_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 = 3
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data = data_layer(name="word", size=dict_dim)
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emb = embedding_layer(input=data, 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.conf
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def outer_step(x):
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outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
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def inner_step(y):
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inner_mem = memory(name="inner_rnn_state",
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size=hidden_dim,
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boot_layer=outer_mem)
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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")
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return out
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inner_rnn_output = recurrent_group(
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step=inner_step,
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name="inner",
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input=x)
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last = last_seq(input=inner_rnn_output, name="outer_rnn_state")
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# "return last" should also work. But currently RecurrentGradientMachine
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# does not handle it, and will report error: In hierachical RNN, all out
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# links should be from sequences now.
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return inner_rnn_output
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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=SubsequenceInput(emb))
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rep = last_seq(input=out)
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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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