import?tensorflow?as?tf
from?Parameters?import?Parameters?as?pm
from?tensorflow.contrib.crf?import?crf_log_likelihood
from?tensorflow.contrib.crf?import?viterbi_decode
from?Data_process?import?batch_iter?process_seq


class?LSTM_CRF（object）:
????def?__init__（self）:
????????self.input_x?=?tf.placeholder（tf.int32?shape=[None?None]?name=‘input_x‘）
????????self.input_y?=?tf.placeholder（tf.int32?shape=[None?None]?name=‘input_y‘）
????????self.seq_length?=?tf.placeholder（tf.int32?shape=[None]?name=‘sequence_length‘）
????????self.keep_pro?=?tf.placeholder（tf.float32?name=‘drop_out‘）
????????self.global_step?=?tf.Variable（0?trainable=False?name=‘global_step‘）

????????self.Model（）

????def?Model（self）:
????????with?tf.device（‘/cpu:0‘）?tf.name_scope（‘embedding‘）:
????????????#從截斷的正態分布中輸出隨機值。?pm.vocab_size?pm.embedding_size表示生成張量的維度，?-0.25是均值，0.25是標準差
????????????embedding_?=?tf.Variable（tf.truncated_normal（[pm.vocab_size?pm.embedding_size]?-0.25?0.25）?name=‘w‘）
????????????print（pm.vocab_size）
????????????#?查找embedding_中的序號為input_x的元素
????????????embedding?=?tf.nn.embedding_lookup（embedding_?self.input_x）
????????????self.embedding?=?tf.nn.dropout（embedding?pm.keep_pro）

????????with?tf.name_scope（‘biLSTM‘）:
????????????cell_fw?=?tf.nn.rnn_cell.LSTMCell（pm.hidden_dim）
????????????cell_bw?=?tf.nn.rnn_cell.LSTMCell（pm.hidden_dim）
????????????outputs?outstates?=?tf.nn.bidirectional_dynamic_rnn（cell_fw=cell_fw?cell_bw=cell_bwinputs=self.embedding
?????????????????????????????????????????????????????????????????sequence_length=self.seq_length?dtype=tf.float32）
????????????outputs?=?tf.concat（outputs?2）#將雙向RNN的結果進行拼接
????????????#outputs三維張量，[batchsizeseq_length2*hidden_dim]

????????with?tf.name_scope（‘output‘）:
????????????s?=?tf.shape（outputs）
????????????output?=?tf.reshape（outputs?[-1?2*pm.hidden_dim]）
????????????#將輸出轉變成one?hot編碼
????????????output?=?tf.layers.dense（output?pm.num_tags）
????????????output?=?tf.contrib.layers.dropout（output?pm.keep_pro）
????????????self.logits?=?tf.reshape（output?[-1?s[1]?pm.num_tags]）

????????with?tf.name_scope（‘crf‘）:
????????????log_likelihood?self.transition_params?=?crf_log_likelihood（inputs=self.logits?tag_indices=self.input_y?sequence_lengths=self.seq_length）
????????????#?log_likelihood是對數似然函數，transition_params是轉移概率矩陣
????????????#crf_log_likelihood{inputs:[batch_size（64）max_seq_lengthnum_tags]
????????????????????????????????#tag_indices:[batch_size（64）max_seq_length]
????????????????????????????????#sequence_lengths:[real_seq_length]
????????????#transition_params:?A?[num_tags（7）?num_tags（7）]?transition?matrix
????????????#log_likelihood:?A?scalar?containing?the?log-likelihood?of?the?given?sequence?of?tag?indices.

????????with?tf.name_scope（‘loss‘）:
????????????self.loss?=?tf.reduce_mean（-log_likelihood）?#最大似然取負，使用梯度下降

????????with?tf.name_scope（‘optimizer‘）:
????????????optimizer?=?tf.train.AdamOptimizer（pm.learn

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件???????1203??2019-01-31?17:08??Bi-LSTM_CRF_NER\.gitignore

?????文件???????5171??2019-05-28?16:48??Bi-LSTM_CRF_NER\biLstm_Crf.py

?????文件????8636940??2019-05-17?17:05??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-3168.data-00000-of-00001

?????文件???????1015??2019-05-17?17:05??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-3168.index

?????文件?????688284??2019-05-17?17:05??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-3168.meta

?????文件????8636940??2019-05-17?17:16??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-6336.data-00000-of-00001

?????文件???????1015??2019-05-17?17:16??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-6336.index

?????文件?????688284??2019-05-17?17:16??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-6336.meta

?????文件????8636940??2019-05-17?17:28??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-9504.data-00000-of-00001

?????文件???????1015??2019-05-17?17:28??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-9504.index

?????文件?????688284??2019-05-17?17:28??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\best_validation-9504.meta

?????文件????????199??2019-05-17?17:28??Bi-LSTM_CRF_NER\checkpoints\biLSTM_Crf\checkpoint

?????文件?????????36??2019-05-28?17:11??Bi-LSTM_CRF_NER\data\eva_data

?????文件??????????3??2019-05-17?18:00??Bi-LSTM_CRF_NER\data\input_data

?????文件????1291634??2019-01-31?17:08??Bi-LSTM_CRF_NER\data\test_data

?????文件???16129425??2019-01-31?17:08??Bi-LSTM_CRF_NER\data\train_data

?????文件??????76143??2019-05-17?16:52??Bi-LSTM_CRF_NER\data\word2id.pkl

?????文件???????4416??2019-05-28?17:43??Bi-LSTM_CRF_NER\Data_process.py

?????文件????????404??2019-05-17?09:19??Bi-LSTM_CRF_NER\Parameters.py

?????文件???????4165??2019-05-28?17:42??Bi-LSTM_CRF_NER\serve.py

?????文件?????586513??2019-01-31?17:08??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1548919100.JTYSL-8304YVB

?????文件?????587478??2019-05-13?16:42??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557736977.BY

?????文件?????587478??2019-05-14?16:54??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557824045.BY

?????文件?????587478??2019-05-15?10:12??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557886360.BY

?????文件?????587478??2019-05-15?10:12??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557886379.BY

?????文件?????587478??2019-05-15?10:43??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557888198.BY

?????文件?????587478??2019-05-15?10:43??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557888221.BY

?????文件?????587478??2019-05-15?10:44??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557888294.BY

?????文件?????587478??2019-05-15?10:55??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557888907.BY

?????文件?????587478??2019-05-15?10:58??Bi-LSTM_CRF_NER\tensorboard\biLSTM_Crf\events.out.tfevents.1557889089.BY

............此處省略25個文件信息