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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Thu Mar 26 09:04:13 2020 | |
| @author: luol2 | |
| """ | |
| import time | |
| import sys | |
| import numpy as np | |
| import keras | |
| from src.nn_represent import CNN_RepresentationLayer,BERT_RepresentationLayer | |
| from keras.layers import * | |
| from keras.models import Model | |
| from keras_bert import load_trained_model_from_checkpoint | |
| class bioTag_CNN(): | |
| def __init__(self, model_files): | |
| self.model_type='cnn' | |
| model_test_type='cnn' | |
| self.fea_dict = {'word': 1, | |
| 'char': 1, | |
| 'lemma':0, | |
| 'pos':0} | |
| self.hyper = {'sen_max' :20, | |
| 'word_max' :40, | |
| 'charvec_size' :50, | |
| 'pos_size' :50} | |
| self.w2vfile=model_files['w2vfile'] | |
| self.charfile=model_files['charfile'] | |
| self.labelfile=model_files['labelfile'] | |
| self.posfile=model_files['posfile'] | |
| vocab={'char':self.charfile,'label':self.labelfile,'pos':self.posfile} | |
| print('loading w2v model.....') | |
| self.rep = CNN_RepresentationLayer(self.w2vfile,vocab_file=vocab, frequency=400000) | |
| print('building model......') | |
| all_fea = [] | |
| fea_list = [] | |
| if self.fea_dict['word'] == 1: | |
| word_input = Input(shape=(self.hyper['sen_max'],), dtype='int32', name='word_input') | |
| all_fea.append(word_input) | |
| word_fea = Embedding(self.rep.vec_table.shape[0], self.rep.vec_table.shape[1], weights=[self.rep.vec_table], trainable=True,mask_zero=False, input_length=self.hyper['sen_max'], name='word_emd')(word_input) | |
| fea_list.append(word_fea) | |
| if self.fea_dict['char'] == 1: | |
| char_input = Input(shape=(self.hyper['sen_max'],self.hyper['word_max']), dtype='int32', name='char_input') | |
| all_fea.append(char_input) | |
| char_fea = TimeDistributed(Embedding(self.rep.char_table_size, self.hyper['charvec_size'], trainable=True,mask_zero=False), name='char_emd')(char_input) | |
| char_fea = TimeDistributed(Conv1D(self.hyper['charvec_size']*2, 3, padding='same',activation='relu'), name="char_cnn")(char_fea) | |
| char_fea_max = TimeDistributed(GlobalMaxPooling1D(), name="char_pooling_max")(char_fea) | |
| fea_list.append(char_fea_max) | |
| if self.fea_dict['lemma'] == 1: | |
| lemma_input = Input(shape=(self.hyper['sen_max'],), dtype='int32', name='lemma_input') | |
| all_fea.append(lemma_input) | |
| lemma_fea = Embedding(self.rep.vec_table.shape[0], self.rep.vec_table.shape[1], weights=[self.rep.vec_table], trainable=True,mask_zero=False, input_length=self.hyper['sen_max'], name='lemma_emd')(lemma_input) | |
| fea_list.append(lemma_fea) | |
| if self.fea_dict['pos'] == 1: | |
| pos_input = Input(shape=(self.hyper['sen_max'],), dtype='int32', name='pos_input') | |
| all_fea.append(pos_input) | |
| pos_fea = Embedding(self.rep.pos_table_size, self.hyper['pos_size'], trainable=True,mask_zero=False, input_length=self.hyper['sen_max'], name='pos_emd')(pos_input) | |
| fea_list.append(pos_fea) | |
| if len(fea_list) == 1: | |
| concate_vec = fea_list[0] | |
| else: | |
| concate_vec = Concatenate()(fea_list) | |
| concate_vec = Dropout(0.4)(concate_vec) | |
| # model | |
| if model_test_type=='cnn': | |
| cnn = Conv1D(1024, 1, padding='valid', activation='relu',name='cnn1')(concate_vec) | |
| cnn = GlobalMaxPooling1D()(cnn) | |
| elif model_test_type=='lstm': | |
| bilstm = Bidirectional(LSTM(200, return_sequences=True, implementation=2, dropout=0.4, recurrent_dropout=0.4), name='bilstm1')(concate_vec) | |
| cnn = GlobalMaxPooling1D()(bilstm) | |
| dense = Dense(1024, activation='relu')(cnn) | |
| dense= Dropout(0.4)(dense) | |
| output = Dense(self.rep.label_table_size, activation='softmax')(dense) | |
| self.model = Model(inputs=all_fea, outputs=output) | |
| def load_model(self,model_file): | |
| self.model.load_weights(model_file) | |
| #self.model.summary() | |
| print('load cnn model done!') | |
| class bioTag_BERT(): | |
| def __init__(self, model_files): | |
| self.model_type='bert' | |
| self.maxlen = 64 | |
| config_path = model_files['config_path'] | |
| checkpoint_path = model_files['checkpoint_path'] | |
| vocab_path = model_files['vocab_path'] | |
| self.label_file=model_files['labelfile'] | |
| self.rep = BERT_RepresentationLayer( vocab_path, self.label_file) | |
| bert_model = load_trained_model_from_checkpoint(config_path, checkpoint_path, training=False, trainable=True,seq_len=self.maxlen) | |
| x1_in = Input(shape=(None,)) | |
| x2_in = Input(shape=(None,)) | |
| x = bert_model([x1_in, x2_in]) | |
| x = Lambda(lambda x: x[:, 0])(x) | |
| outputs = Dense(self.rep.label_table_size, activation='softmax')(x) | |
| self.model = Model(inputs=[x1_in,x2_in], outputs=outputs) | |
| def load_model(self,model_file): | |
| self.model.load_weights(model_file) | |
| #self.model.summary() | |
| class bioTag_Bioformer(): | |
| def __init__(self, model_files): | |
| self.model_type='bioformer' | |
| self.maxlen = 32 | |
| config_path = model_files['config_path'] | |
| checkpoint_path = model_files['checkpoint_path'] | |
| vocab_path = model_files['vocab_path'] | |
| self.label_file=model_files['labelfile'] | |
| self.rep = BERT_RepresentationLayer( vocab_path, self.label_file) | |
| bert_model = load_trained_model_from_checkpoint(config_path, checkpoint_path, training=False, trainable=True,seq_len=self.maxlen) | |
| x1_in = Input(shape=(None,)) | |
| x2_in = Input(shape=(None,)) | |
| x = bert_model([x1_in, x2_in]) | |
| x = Lambda(lambda x: x[:, 0])(x) | |
| outputs = Dense(self.rep.label_table_size, activation='softmax')(x) | |
| self.model = Model(inputs=[x1_in,x2_in], outputs=outputs) | |
| def load_model(self,model_file): | |
| self.model.load_weights(model_file) | |
| #self.model.summary() | |
| print('load bioformer model done!') | |