sequence_recurrent.py 2.4 KB
Newer Older
D
dzhwinter 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13
#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#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
#limitations under the License.
14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69
#!/usr/bin/env python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# 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
# limitations under the License.

from paddle.trainer_config_helpers import *

######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
    dict_file[line.strip()] = line_count

define_py_data_sources2(
    train_list='gserver/tests/Sequence/train.list',
    test_list=None,
    module='sequenceGen',
    obj='process',
    args={"dict_file": dict_file})

settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 128
label_dim = 3

# This config is designed to be equivalent with sequence_recurrent_group.py

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

emb = embedding_layer(
    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))

recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())

recurrent_last = last_seq(input=recurrent)

with mixed_layer(
        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
    output += full_matrix_projection(input=recurrent_last)

outputs(
    classification_cost(
        input=output, label=data_layer(
            name="label", size=1)))