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MVIFSA_eval.py
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# coding: utf-8
import json
import numpy as np
from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Bidirectional, Add, MaxPooling1D, Concatenate, Dot, Flatten
from tensorflow.keras import Model
from keras import backend as K
import os
import tensorflow as tf
from configparser import ConfigParser
from tensorflow.keras.optimizers import Adam
from preprocess import readData
from preprocess import readRelation
from layer.attention import Attention
def ranking_loss(y_true, y_pred):
return K.maximum(0.0, 0.1 + K.sum(y_pred*y_true,axis=-1))
def model_construct():
# CONFIG
config = ConfigParser()
config.read('./config.ini')
question_input = Input(shape=(config.getint('pre', 'question_maximum_length'), ), dtype='int32',name="question_input")
relation_all_input = Input(shape=(config.getint('pre', 'relation_word_maximum_length'), ), dtype='int32',name="relation_all_input")
relation_input = Input(shape=(config.getint('pre', 'relation_maximum_length'), ), dtype='int32',name="relation_input")
question_emd = np.load('./question_emd_matrix.npy')
relation_emd = np.load('./relation_emd_matrix.npy')
relation_all_emd = np.load('./relation_all_emd_matrix.npy')
question_emd = Embedding(question_emd.shape[0],
config.getint('pre', 'word_emd_length'),
weights=[question_emd],
input_length=config.getint('pre', 'question_maximum_length'),
trainable=False,name="question_emd")(question_input)
sharedEmbd_r_w = Embedding(relation_all_emd.shape[0],
config.getint('pre', 'word_emd_length'),
weights=[relation_all_emd],
input_length=config.getint('pre', 'relation_word_maximum_length'),
trainable=False,name="sharedEmbd_r_w")
relation_word_emd = sharedEmbd_r_w(relation_all_input)
sharedEmbd_r = Embedding(relation_emd.shape[0],
config.getint('pre', 'word_emd_length'),
weights=[relation_emd],
input_length=config.getint('pre', 'relation_maximum_length'),
trainable=True,name="sharedEmbd_r")
relation_emd = sharedEmbd_r(relation_input)
# 2. Information Fusion layer1
# question bilstm1
bilstem_layer_1 = Bidirectional(LSTM(units=40, return_sequences=True, implementation=2), name="bilstm_layer1")
question_bilstm_1 = bilstem_layer_1(question_emd)
# relation word bilstm1
relation_word_bilstm_1 = bilstem_layer_1(relation_word_emd)
# relation bilstm1
relation_bilstm_1 = bilstem_layer_1(relation_emd)
# 3. Complex Information Representation Layer
bilstem_layer_2 = Bidirectional(LSTM(units=40, return_sequences=True, implementation=2), name="bilstm_layer2")
# question bilstm2
question_bilstm_2 = bilstem_layer_2(question_bilstm_1)
bilstem_layer_3 = Bidirectional(LSTM(units=40, return_sequences=True, implementation=2), name="bilstm_layer3")
# relation word bilstm3
relation_word_bilstm_3 = bilstem_layer_3(relation_word_bilstm_1)
# relation bilstm3
relation_bilstm_3 = bilstem_layer_3(relation_bilstm_1)
# 4. Residual Learning Layer
# question residual connect
question_connect = Add()([question_bilstm_1, question_bilstm_2])
question_rl_out = MaxPooling1D(80, padding='same')(question_connect)
# relation word residual connect
relation_word_connect = Add()([relation_word_bilstm_1, relation_word_bilstm_3])
relation_word_rc_out = MaxPooling1D(80, padding='same')(relation_word_connect)
# relation residual connect
relation_connect = Add()([relation_bilstm_1, relation_bilstm_3])
relation_rc_out = MaxPooling1D(80, padding='same')(relation_connect)
relation_rl_out = Add()([relation_rc_out, relation_word_rc_out])
# 5. Self-Attention Layer
self_attention = Attention(80)
question_sa_out = self_attention(question_rl_out)
relation_sa_out = self_attention(relation_rl_out)
# finally COSINE SIMILARITY
question_flatten = Flatten()(question_sa_out)
relation_flatten = Flatten()(relation_sa_out)
result = Dot(axes=-1, normalize=True)([question_flatten, relation_flatten])
model = Model(inputs=[question_input, relation_input, relation_all_input,], outputs=result)
model.compile(optimizer=Adam(), loss=ranking_loss)
return model
if __name__ == '__main__':
# GPU settings
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
neg_num = json.load(open('./neg_number.json', 'r'))
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
print(e)
model = model_construct()
model.load_weights('./model/my_model_weights.h5')
print(model.summary())
question_feature = np.load('./test_question_feature.npy')
relation_feature = np.load('./test_relation_feature.npy')
relation_all_feature = np.load('./test_relation_all_feature.npy')
print('positive data loaded...')
simi_pos = model.predict([question_feature, relation_feature, relation_all_feature], batch_size=1024)
print('positive similarity computed...')
np.save('test_pre_pos.npy', simi_pos)
relation_feature_neg = np.load('./test_relation_feature_neg.npy')
relation_all_feature_neg = np.load('./test_relation_all_feature_neg.npy')
print('negtive data loaded...')
simi_neg = model.predict([question_feature, relation_feature_neg, relation_all_feature_neg], batch_size=1024)
print('negtive similarity computed...')
np.save('test_pre_neg.npy', simi_neg)
acc = np.sum(simi_pos>simi_neg) / simi_pos.shape[0]
print("relation accurcy: " + str(acc))
index = 0
false_list = list()
true_list = list()
all_set = set()
config = ConfigParser()
config.read('./config.ini')
data = readData(config.get('pre', 'test_filepath'))
relation = readRelation(config.get('pre', 'relation_filepath'))
for num,neg_index in neg_num:
if np.sum(simi_pos[index: index+num]-simi_neg[index: index+num]<0) > 0:
false_list.append(neg_index)
print (simi_pos[index])
print (np.max(simi_neg[index: index+num]))
print (len(simi_neg[index: index+num]))
print (np.argmax(simi_neg[index: index+num]))
print (simi_neg[index: index+num][np.argmax(simi_neg[index: index+num])])
print (neg_index)
print ("")
pass
else:
true_list.append(neg_index)
index += num
all_set.add(neg_index)
print (max(true_list))
true_list = set([i for i in true_list if i in all_set and i not in false_list])
print (len(all_set))
print (len(true_list))
for i in all_set:
if i not in true_list:
print (i)
print (data[0])
print (relation[0][1])
print (relation[1][1])
print('sentence accurcy: '+str(len(true_list)/len(all_set)))