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67 lines
2.3 KiB
67 lines
2.3 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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dict_file = "./data/dict.txt"
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word_dict = dict()
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with open(dict_file, 'r') as f:
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for i, line in enumerate(f):
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w = line.strip().split()[0]
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word_dict[w] = i
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is_predict = get_config_arg('is_predict', bool, False)
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trn = 'data/train.list' if not is_predict else None
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tst = 'data/test.list' if not is_predict else 'data/pred.list'
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process = 'process' if not is_predict else 'process_predict'
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define_py_data_sources2(train_list=trn,
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test_list=tst,
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module="dataprovider_emb",
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obj=process,
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args={"dictionary": word_dict})
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batch_size = 128 if not is_predict else 1
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settings(
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batch_size=batch_size,
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learning_rate=2e-3,
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learning_method=AdamOptimizer(),
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regularization=L2Regularization(8e-4),
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gradient_clipping_threshold=25
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)
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bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
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data = data_layer(name="word", size=len(word_dict))
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emb = embedding_layer(input=data, size=128)
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fc = fc_layer(input=emb, size=512,
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act=LinearActivation(),
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bias_attr=bias_attr,
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layer_attr=ExtraAttr(drop_rate=0.1))
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lstm = lstmemory(input=fc, act=TanhActivation(),
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bias_attr=bias_attr,
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layer_attr=ExtraAttr(drop_rate=0.25))
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lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
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output = fc_layer(input=lstm_last, size=2,
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bias_attr=bias_attr,
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act=SoftmaxActivation())
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if is_predict:
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maxid = maxid_layer(output)
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outputs([maxid, output])
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else:
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label = data_layer(name="label", size=2)
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cls = classification_cost(input=output, label=label)
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outputs(cls)
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