sequence_nest_rnn.conf

#edit-mode: -*- python -*-
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from paddle.trainer_config_helpers import *

######################## data source ################################
define_py_data_sources2(train_list='legacy/gserver/tests/Sequence/dummy.list',
                        test_list=None,
                        module='rnn_data_provider',
                        obj='process_subseq')


settings(batch_size=2, learning_rate=0.01)
######################## network configure ################################
dict_dim = 10
word_dim = 8
hidden_dim = 8
label_dim = 3

data = data_layer(name="word", size=dict_dim)

emb = embedding_layer(input=data, size=word_dim)

# This hierachical RNN is designed to be equivalent to the simple RNN in
# sequence_rnn.conf

def outer_step(x):
    outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
    def inner_step(y):
        inner_mem = memory(name="inner_rnn_state",
                           size=hidden_dim,
                           boot_layer=outer_mem)
        out = fc_layer(input=[y, inner_mem],
                        size=hidden_dim,
                        act=TanhActivation(),
                        bias_attr=True,
                        name="inner_rnn_state")
        return out

    inner_rnn_output = recurrent_group(
        step=inner_step,
        name="inner",
        input=x)
    last = last_seq(input=inner_rnn_output, name="outer_rnn_state")

    # "return last" won't work, because recurrent_group only support the input 
    # sequence type is same as return sequence type.
    return inner_rnn_output

out = recurrent_group(
    name="outer",
    step=outer_step,
    input=SubsequenceInput(emb))

rep = last_seq(input=out)
prob = fc_layer(size=label_dim,
                input=rep,
                act=SoftmaxActivation(),
                bias_attr=True)

outputs(classification_cost(input=prob,
                            label=data_layer(name="label", size=label_dim)))