def train():
print("重新训练，请保证计算机拥有至少8G空闲内存与2G空闲显存")
# 准备训练数据
print("正在准备训练数据，大约需要五分钟...")
qTrain, aTrain, lTrain, qIdTrain = qaData.loadData(trainingFile, word2idx,
unrollSteps, True)
qDevelop, aDevelop, lDevelop, qIdDevelop = qaData.loadData(
developFile, word2idx, unrollSteps, True)
trainQuestionCounts = qIdTrain[-1]
for i in range(len(qIdDevelop)):
qIdDevelop[i] += trainQuestionCounts
tqs, tta, tfa = [], [], []
for question, trueAnswer, falseAnswer in qaData.trainingBatchIter(
qTrain + qDevelop, aTrain + aDevelop, lTrain + lDevelop,
qIdTrain + qIdDevelop, batchSize):
tqs.append(question), tta.append(trueAnswer), tfa.append(falseAnswer)
print("加载完成！")
# 开始训练
print("开始训练，全部训练过程大约需要12小时")
sess.run(tf.global_variables_initializer())
lr = learningRate # 引入局部变量，防止shadow name
for i in range(lrDownCount):
optimizer = tf.train.GradientDescentOptimizer(lr)
optimizer.apply_gradients(zip(grads, tvars))
trainOp = optimizer.apply_gradients(zip(grads, tvars),
global_step=globalStep)
for epoch in range(epochs):
for question, trueAnswer, falseAnswer in zip(tqs, tta, tfa):
startTime = time.time()
feed_dict = {
lstm.inputQuestions: question,
lstm.inputTrueAnswers: trueAnswer,
lstm.inputFalseAnswers: falseAnswer,
lstm.keep_prob: dropout
}
_, step, _, _, loss = \
sess.run([trainOp, globalStep, lstm.trueCosSim, lstm.falseCosSim, lstm.loss], feed_dict)
timeUsed = time.time() - startTime
print("step:", step, "loss:", loss, "time:", timeUsed)
saver.save(sess, saveFile)
lr *= lrDownRate
def train():
print("重新训练，请保证计算机拥有至少8G空闲内存与2G空闲显存")
# 准备训练数据
print("正在准备训练数据，大约需要五分钟...")
qTrain, aTrain, lTrain, qIdTrain = qaData.loadData(trainingFile, word2idx, unrollSteps, True)
qDevelop, aDevelop, lDevelop, qIdDevelop = qaData.loadData(developFile, word2idx, unrollSteps, True)
trainQuestionCounts = qIdTrain[-1]
for i in range(len(qIdDevelop)):
qIdDevelop[i] += trainQuestionCounts
tqs, tta, tfa = [], [], []
for question, trueAnswer, falseAnswer in qaData.trainingBatchIter(qTrain + qDevelop, aTrain + aDevelop,
lTrain + lDevelop, qIdTrain + qIdDevelop,
batchSize):
tqs.append(question), tta.append(trueAnswer), tfa.append(falseAnswer)
print("加载完成！")
# 开始训练
print("开始训练，全部训练过程大约需要12小时")
sess.run(tf.global_variables_initializer())
lr = learningRate # 引入局部变量，防止shadow name
for i in range(lrDownCount):
optimizer = tf.train.GradientDescentOptimizer(lr)
optimizer.apply_gradients(zip(grads, tvars))
trainOp = optimizer.apply_gradients(zip(grads, tvars), global_step=globalStep)
for epoch in range(epochs):
for question, trueAnswer, falseAnswer in zip(tqs, tta, tfa):
startTime = time.time()
feed_dict = {
lstm.inputQuestions: question,
lstm.inputTrueAnswers: trueAnswer,
lstm.inputFalseAnswers: falseAnswer,
lstm.keep_prob: dropout
}
_, step, _, _, loss = \
sess.run([trainOp, globalStep, lstm.trueCosSim, lstm.falseCosSim, lstm.loss], feed_dict)
timeUsed = time.time() - startTime
print("step:", step, "loss:", loss, "time:", timeUsed)
saver.save(sess, saveFile)
lr *= lrDownRate
def train():
print("重新训练，请保证计算机拥有至少8G空闲内存与2G空闲显存")
# 准备训练数据
print("正在准备训练数据，大约需要五分钟...")
qTrain, aTrain, lTrain, qIdTrain = qaData.loadData(trainingFile, word2idx,
unrollSteps, True)
tqs, tta, tfa = [], [], []
for question, trueAnswer, falseAnswer in qaData.trainingBatchIter(
qTrain, aTrain, lTrain, qIdTrain, batchSize):
tqs.append(question), tta.append(trueAnswer), tfa.append(falseAnswer)
tqs = np.array(tqs)
tta = np.array(tta)
tfa = np.array(tfa)
print("加载完成！")
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(tqs)))
tqs_shuffled = tqs[shuffle_indices]
tta_shuffled = tta[shuffle_indices]
tfa_shuffled = tfa[shuffle_indices]
# Split train/test set
# TODO: This is very crude, should use cross-validation
dev_sample_index = -1 * int(0.1 * float(len(tqs)))
tqs_train, tqs_dev = tqs_shuffled[:dev_sample_index], tqs_shuffled[
dev_sample_index:]
tta_train, tta_dev = tta_shuffled[:dev_sample_index], tta_shuffled[
dev_sample_index:]
tfa_train, tfa_dev = tfa_shuffled[:dev_sample_index], tfa_shuffled[
dev_sample_index:]
print("开始训练，全部训练过程大约需要12小时")
sess.run(tf.global_variables_initializer())
lr = learningRate # 引入局部变量，防止shadow name
for i in range(lrDownCount):
optimizer = tf.train.GradientDescentOptimizer(lr)
optimizer.apply_gradients(zip(grads, tvars))
trainOp = optimizer.apply_gradients(zip(grads, tvars),
global_step=globalStep)
for epoch in range(epochs):
for question, trueAnswer, falseAnswer in zip(
tqs_train, tta_train, tfa_train):
startTime = time.time()
feed_dict = {
lstm.inputQuestions: question,
lstm.inputTrueAnswers: trueAnswer,
lstm.inputFalseAnswers: falseAnswer,
lstm.keep_prob: dropout
}
_, step, _, _, loss = \
sess.run([trainOp, globalStep, lstm.trueCosSim, lstm.falseCosSim, lstm.loss], feed_dict)
timeUsed = time.time() - startTime
print("step:", step, "loss:", loss, "time:", timeUsed)
saver.save(sess, saveFile)
lr *= lrDownRate
with open(resultFile, 'w') as file:
for question, answer in zip(tqs_dev, tta_dev):
feed_dict = {
lstm.inputTestQuestions: question,
lstm.inputTestAnswers: answer,
lstm.keep_prob: dropout
}
_, scores = sess.run([globalStep, lstm.result], feed_dict)
for score in scores:
print("%.9f" % score + '\n')
print("------------------------------------------")
for question, answer in zip(tqs_dev, tfa_dev):
feed_dict = {
lstm.inputTestQuestions: question,
lstm.inputTestAnswers: answer,
lstm.keep_prob: dropout
}
_, scores = sess.run([globalStep, lstm.result], feed_dict)
for score in scores:
print("%.9f" % score + '\n')
epochs = 20 # 每次学习速度指数下降之前执行的完整epoch次数
batchSize = 20 # 每一批次处理的<b>问题</b>个数
rnnSize = 100 # LSTM cell中隐藏层神经元的个数
margin = 0.1 # M is constant margin
unrollSteps = 100 # 句子中的最大词汇数目
max_grad_norm = 5 # 用于控制梯度膨胀，如果梯度向量的L2模超过max_grad_norm，则等比例缩小
allow_soft_placement = True # Allow device soft device placement
gpuMemUsage = 0.75 # 显存最大使用率
# 读取测试数据
print("正在载入测试数据，大约需要一分钟...")
embedding, word2idx = qaData.loadEmbedding(embeddingFile)
qTest, aTest, _, qIdTest = qaData.loadData(trainingFile, word2idx,
unrollSteps)
print("测试数据加载完成")
# 配置TensorFlow
with tf.Graph().as_default(), tf.device('/cpu:0'):
session_conf = tf.ConfigProto(
allow_soft_placement=allow_soft_placement)
with tf.Session(config=session_conf).as_default() as sess:
# 加载LSTM网络
print("正在加载LSTM网络，大约需要三分钟...")
globalStep = tf.Variable(0, name="globle_step", trainable=False)
lstm = QaLSTMNet(batchSize, unrollSteps, embedding, embeddingSize,
rnnSize, margin)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(lstm.loss, tvars),
max_grad_norm)
saver = tf.train.Saver()
batchSize = 20 # 每一批次处理的<b>问题</b>个数
rnnSize = 100 # LSTM cell中隐藏层神经元的个数
margin = 0.1 # M is constant margin
unrollSteps = 100 # 句子中的最大词汇数目
max_grad_norm = 5 # 用于控制梯度膨胀，如果梯度向量的L2模超过max_grad_norm，则等比例缩小
allow_soft_placement = True # Allow device soft device placement
gpuMemUsage = 0.75 # 显存最大使用率
gpuDevice = "/gpu:0" # GPU设备名
# 读取测试数据
print("正在载入测试数据，大约需要一分钟...")
embedding, word2idx = qaData.loadEmbedding(embeddingFile)
qTest, aTest, _, qIdTest = qaData.loadData(testingFile, word2idx, unrollSteps)
print("测试数据加载完成")
# 配置TensorFlow
with tf.Graph().as_default(), tf.device(gpuDevice):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpuMemUsage)
session_conf = tf.ConfigProto(allow_soft_placement=allow_soft_placement, gpu_options=gpu_options)
with tf.Session(config=session_conf).as_default() as sess:
# 加载LSTM网络
print("正在加载LSTM网络，大约需要三分钟...")
globalStep = tf.Variable(0, name="globle_step", trainable=False)
lstm = QaLSTMNet(batchSize, unrollSteps, embedding, embeddingSize, rnnSize, margin)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(lstm.loss, tvars), max_grad_norm)
saver = tf.train.Saver()
print("加载完成！")
