import tensorflow as tf
class QaLSTMNet(object):
"""
中文问答系统使用的LSTM网络结构
"""
def __init__(self, batchSize, unrollSteps, embeddings, embeddingSize, rnnSize, margin):
self.batchSize = batchSize
self.unrollSteps = unrollSteps
self.embeddings = embeddings
self.embeddingSize = embeddingSize
self.rnnSize = rnnSize
self.margin = margin
self.keep_prob = tf.placeholder(tf.float32, name="keep_drop")
self.inputQuestions = tf.placeholder(tf.int32, shape=[None, self.unrollSteps])
self.inputTrueAnswers = tf.placeholder(tf.int32, shape=[None, self.unrollSteps])
self.inputFalseAnswers = tf.placeholder(tf.int32, shape=[None, self.unrollSteps])
self.inputTestQuestions = tf.placeholder(tf.int32, shape=[None, self.unrollSteps])
self.inputTestAnswers = tf.placeholder(tf.int32, shape=[None, self.unrollSteps])
# 设置word embedding层
with tf.device("/cpu:0"), tf.name_scope("embedding_layer"):
tfEmbedding = tf.Variable(tf.to_float(self.embeddings), trainable=True, name="W")
questions = tf.nn.embedding_lookup(tfEmbedding, self.inputQuestions)
trueAnswers = tf.nn.embedding_lookup(tfEmbedding, self.inputTrueAnswers)
falseAnswers = tf.nn.embedding_lookup(tfEmbedding, self.inputFalseAnswers)
testQuestions = tf.nn.embedding_lookup(tfEmbedding, self.inputTestQuestions)
testAnswers = tf.nn.embedding_lookup(tfEmbedding, self.inputTestAnswers)
# 建立LSTM网络
with tf.variable_scope("LSTM_scope", reuse=None):
question1 = self.biLSTMCell(questions, self.rnnSize)
question2 = tf.nn.tanh(self.max_pooling(question1))
with tf.variable_scope("LSTM_scope", reuse=True):
trueAnswer1 = self.biLSTMCell(trueAnswers, self.rnnSize)
trueAnswer2 = tf.nn.tanh(self.max_pooling(trueAnswer1))
falseAnswer1 = self.biLSTMCell(falseAnswers, self.rnnSize)
falseAnswer2 = tf.nn.tanh(self.max_pooling(falseAnswer1))
testQuestion1 = self.biLSTMCell(testQuestions, self.rnnSize)
testQuestion2 = tf.nn.tanh(self.max_pooling(testQuestion1))
testAnswer1 = self.biLSTMCell(testAnswers, self.rnnSize)
testAnswer2 = tf.nn.tanh(self.max_pooling(testAnswer1))
self.trueCosSim = self.getCosineSimilarity(question2, trueAnswer2)
self.falseCosSim = self.getCosineSimilarity(question2, falseAnswer2)
self.loss = self.getLoss(self.trueCosSim, self.falseCosSim, self.margin)
self.result = self.getCosineSimilarity(testQuestion2, testAnswer2)
@staticmethod
def biLSTMCell(x, hiddenSize):
input_x = tf.transpose(x, [1, 0, 2])
input_x = tf.unstack(input_x)
lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(hiddenSize, forget_bias=1.0, state_is_tuple=True)
lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(hiddenSize, forget_bias=1.0, state_is_tuple=True)
output, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, input_x, dtype=tf.float32)
output = tf.stack(output)
output = tf.transpose(output, [1, 0, 2])
return output
@staticmethod
def getCosineSimilarity(q, a):
q1 = tf.sqrt(tf.reduce_sum(tf.multiply(q, q), 1))
a1 = tf.sqrt(tf.reduce_sum(tf.multiply(a, a), 1))
mul = tf.reduce_sum(tf.multiply(q, a), 1)
cosSim = tf.div(mul, tf.multiply(q1, a1))
return cosSim
@staticmethod
def max_pooling(lstm_out):
height = int(lstm_out.get_shape()[1])
width = int(lstm_out.get_shape()[2])
lstm_out = tf.expand_dims(lstm_out, -1)
output = tf.nn.max_pool(lstm_out, ksize=[1, height, 1, 1], strides=[1, 1, 1, 1], padding='VALID')
output = tf.reshape(output, [-1, width])
return output
@staticmethod
def getLoss(trueCosSim, falseCosSim, margin):
zero = tf.fill(tf.shape(trueCosSim), 0.0)
tfMargin = tf.fill(tf.shape(trueCosSim), margin)
with tf.name_scope("loss"):
losses = tf.maximum(zero, tf.subtract(tfMargin, tf.subtract(trueCosSim, falseCosSim)))
loss = tf.reduce_sum(losses)
return loss
