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qaLSTMNet.py
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import tensorflow as tf
class QaLSTMNet(object):
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])
# print("设置词向量层")
# 设置word embedding层
# 使用with，也就是python的上下文管理器，执行会会自动关闭会话，释放内存，简单高效！
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)
# 选取一个张量里面索引对应的元素
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)
# print("建立BiLSTM网络1")
# 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))
# print("建立BiLSTM网络2")
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)
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", self.loss)
# Dev summaries
self.dev_summary_op = tf.summary.merge([loss_summary])
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)))
# 损失函数
loss = tf.reduce_sum(losses)
# 用于计算张量tensor沿着某一维度的和，可以在求和后降维。
return loss
#
# import tensorflow as tf
#
#
# class QaLSTMNet(object):
#
# 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])
# # print("设置词向量层")
# # 设置word embedding层
# # 使用with，也就是python的上下文管理器，执行会会自动关闭会话，释放内存，简单高效！
# with tf.device("/cpu:0"), tf.name_scope("embedding_layer"):
# tfEmbedding = tf.Variable(tf.to_float(self.embeddings), trainable=True, name="W")
# # print("----tfEmbedding.shape = ",tfEmbedding.shape)
# questions = tf.nn.embedding_lookup(tfEmbedding, self.inputQuestions)
# # print("----questions.shape = ",questions.shape)
# trueAnswers = tf.nn.embedding_lookup(tfEmbedding, self.inputTrueAnswers)
# # print("----trueAnswers.shape = ",trueAnswers.shape)
# falseAnswers = tf.nn.embedding_lookup(tfEmbedding, self.inputFalseAnswers)
# # print("----falseAnswers.shape = ",falseAnswers.shape)
# testQuestions = tf.nn.embedding_lookup(tfEmbedding, self.inputTestQuestions)
# testAnswers = tf.nn.embedding_lookup(tfEmbedding, self.inputTestAnswers)
# # print("建立BiLSTM网络1")
# # 建立LSTM网络
# with tf.variable_scope("LSTM_scope", reuse=None):
# question1 = self.biLSTMCell(questions, self.rnnSize)
# # print("~~~~~question1.shape = ",question1.shape)
# question2 = tf.nn.tanh(self.max_pooling(question1))
# # print("~~~~~question2.shape = ",question2.shape)
# # print("建立BiLSTM网络2")
# with tf.variable_scope("LSTM_scope", reuse=True):
# trueAnswer1 = self.biLSTMCell(trueAnswers, self.rnnSize)
# # print("~~~~~trueAnswer1.shape = ",trueAnswer1.shape)
# trueAnswer2 = tf.nn.tanh(self.max_pooling(trueAnswer1))
# # print("~~~~~trueAnswer2.shape = ",trueAnswer2.shape)
# falseAnswer1 = self.biLSTMCell(falseAnswers, self.rnnSize)
# # print("~~~~~falseAnswer1.shape = ",falseAnswer1.shape)
# falseAnswer2 = tf.nn.tanh(self.max_pooling(falseAnswer1))
# # print("~~~~~falseAnswer2.shape = ",falseAnswer2.shape)
#
# 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)
# # Summaries for loss and accuracy
# loss_summary = tf.summary.scalar("loss", self.loss)
# # Dev summaries
# self.dev_summary_op = tf.summary.merge([loss_summary])
#
# self.result = self.getCosineSimilarity(testQuestion2, testAnswer2)
#
# @staticmethod
# def biLSTMCell(x, hiddenSize):
# input_x = tf.transpose(x, [1, 0, 2])# 0 1 2 变成1 0 2，最外面两个维度转置
# input_x = tf.unstack(input_x) #多维变成低维，默认axio = 0，最外层的维度
# 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) #一维变成多维
# 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)
# # 增加一个维度，如2*3变成1*2*3，第二个参数是增加的维度的位置，是1*2*3还是2*1*3还是2*3*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)))
# # 损失函数
# loss = tf.reduce_sum(losses)
# # 用于计算张量tensor沿着某一维度的和，可以在求和后降维。
# return loss