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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!')
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