def demo_backend(self, model, config, run_event):
global query, response
with open(config.word_dictionary, "r") as fh:
word_dictionary = json.load(fh)
with open(config.char_dictionary, "r") as fh:
char_dictionary = json.load(fh)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with model.graph.as_default():
with tf.Session(config=sess_config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.save_dir))
if config.decay < 1.0:
sess.run(model.assign_vars)
while run_event.is_set():
sleep(0.1)
if query:
context = word_tokenize(query[0].replace("''", '" ').replace("``", '" '))
c,ch,q,qh,co,qo = convert_to_features(config, query, word_dictionary, char_dictionary, Batcher(config.elmo_vocab_file, config.cont_char_limit))
fd = {'context:0': [c],
'question:0': [q],
'context_char:0': [ch],
'question_char:0': [qh],
'context_cont:0': [co],
'question_cont:0': [qo]}
yp1,yp2 = sess.run([model.yp1, model.yp2], feed_dict = fd)
yp2[0] += 1
response = " ".join(context[yp1[0]:yp2[0]])
query = []
def demo_backend(self, model, config, run_event):
global query, response
with open(config.word_dictionary, "r") as fh:
word_dictionary = json.load(fh)
with open(config.char_dictionary, "r") as fh:
char_dictionary = json.load(fh)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with model.graph.as_default():
with tf.Session(config=sess_config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.save_dir))
if config.decay < 1.0:
sess.run(model.assign_vars)
while run_event.is_set():
sleep(0.1)
if query:
context = word_tokenize(query[0].replace("''", '" ').replace("``", '" '))
c,ch,q,qh = convert_to_features(config, query, word_dictionary, char_dictionary)
fd = {'context:0': [c],
'question:0': [q],
'context_char:0': [ch],
'question_char:0': [qh]}
yp1,yp2 = sess.run([model.yp1, model.yp2], feed_dict = fd)
yp2[0] += 1
response = " ".join(context[yp1[0]:yp2[0]])
query = []
def answer():
context = bottle.request.forms.get('context')
c, q, context_num, batch_size = convert_to_features(config, context, token2idx)
fd = {'context:0': c,
'query:0': q,
'batch_size:0': batch_size}
pred = sess.run([model.pred], feed_dict=fd)
pred = np.sum(np.array(pred).reshape([context_num, 10]), 0)
pred = ''.join(pred.astype(bool).astype(int).astype(str))
return pred
def demo_backend(self, model, config, run_event):
global query, response
with open(config.word_dictionary, "r") as fh:
word_dictionary = json.load(fh)
with open(config.char_dictionary, "r") as fh:
char_dictionary = json.load(fh)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with model.graph.as_default():
with tf.Session(config=sess_config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess,
tf.train.latest_checkpoint(config.save_dir))
if config.decay < 1.0:
sess.run(model.assign_vars)
while run_event.is_set():
sleep(0.1)
if query:
context = word_tokenize(query[0].replace(
"''", '" ').replace("``", '" '))
c, ch, q, qh = convert_to_features(
config, query, word_dictionary, char_dictionary)
fd = {
'context:0': [c],
'question:0': [q],
'context_char:0': [ch],
'question_char:0': [qh]
}
yp1, yp2, logits1, logits2 = sess.run([
model.yp1, model.yp2, model.logits1, model.logits2
],
feed_dict=fd)
log_prob1 = logits1[0][yp1[0]]
log_prob2 = logits2[0][yp2[0]]
score1 = 1 / (1 + math.exp(-log_prob1))
score2 = 1 / (1 + math.exp(-log_prob2))
print("SV Confidence: ", score1, "EV Confidence: ",
score2)
yp2[0] += 1
response = " ".join(context[yp1[0]:yp2[0]])
query = []
def get_pred(self, context):
keras.backend.clear_session() # 清除原本数据
config = flags.FLAGS
cs = convert_to_features(config, context, self.token2idx)
pred = np.array
for i, c in enumerate(cs):
fd = {
'context:0': np.expand_dims(c, 0),
'pre_query:0': self.q_rnn,
'pre_query_mask:0': self.q_mask,
'batch_size:0': 1
}
p = self.sess.run([self.model_yunnan.pred_probability],
feed_dict=fd)
if i == 0:
pred = p
else:
pred = np.concatenate((pred, p), axis=0)
return pred
