training_chatbot.py

# -*- coding：utf-8 -*-
# -*- python3.5

import os
# os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import sys
import numpy as np
import tensorflow as tf
from tensorflow.contrib.legacy_seq2seq.python.ops import seq2seq
import word_token
import jieba
import random
import bm25_fitness_data

size = 8               # LSTM神经元size
GO_ID = 1              # 输出序列起始标记
EOS_ID = 2             # 结尾标记
PAD_ID = 0             # 空值填充0
min_freq = 1           # 样本频率超过这个值才会存入词表
epochs = 12000         # 训练次数
batch_num = 1000       # 参与训练的问答对个数
input_seq_len = 25         # 输入序列长度
output_seq_len = 50        # 输出序列长度
init_learning_rate = 0.5     # 初始学习率


wordToken = word_token.WordToken()   # 这是个词袋模型

# 放在全局的位置，为了动态算出 num_encoder_symbols 和 num_decoder_symbols
max_token_id = wordToken.load_file_list(['./samples/question', './samples/answer'], min_freq)
num_encoder_symbols = max_token_id + 5    # 算上加上填充、结尾标记、输出标记
num_decoder_symbols = max_token_id + 5


def get_id_list_from(sentence):
    """
    得到分词后的ID
    """
    sentence_id_list = []
    seg_list = jieba.cut(sentence)
    for str in seg_list:
        id = wordToken.word2id(str)
        if id:
            sentence_id_list.append(wordToken.word2id(str))
    return sentence_id_list


def get_train_set():
    """
    得到训练问答集
    """
    global num_encoder_symbols, num_decoder_symbols
    train_set = []
    with open('C:/Code/KnowledgeGraph-QA-master/seqdata/question.txt', 'r', encoding='utf-8') as question_file:
        with open('C:/Code/KnowledgeGraph-QA-master/seqdata/answer.txt', 'r', encoding='utf-8') as answer_file:
            while True:
                question = question_file.readline()
                answer = answer_file.readline()
                if question and answer:
                    # strip()方法用于移除字符串头尾的字符
                    question = question.strip()
                    answer = answer.strip()
                    # 得到分词ID
                    question_id_list = get_id_list_from(question)
                    answer_id_list = get_id_list_from(answer)
                    if len(question_id_list) > 0 and len(answer_id_list) > 0:
                        answer_id_list.append(EOS_ID)                  # 回答的后面加EOS_ID=2
                        train_set.append([question_id_list, answer_id_list])
                else:
                    break
    return train_set                # 一个大列表嵌套着问答对小列表


def get_samples(train_set, batch_num):
    """
    构造样本数据:传入的train_set是处理好的问答集
    batch_num:让train_set训练集里多少问答对参与训练
    encoder_inputs： []
    """
    raw_encoder_input = []
    raw_decoder_input = []
    if batch_num >= len(train_set):
        batch_train_set = train_set
    else:
        random_start = random.randint(0, len(train_set)-batch_num)
        batch_train_set = train_set[random_start:random_start+batch_num]

    # 添加起始标记、结束填充
    for sample in batch_train_set:
        raw_encoder_input.append([PAD_ID] * (input_seq_len - len(sample[0])) + sample[0])
        raw_decoder_input.append([GO_ID] + sample[1] + [PAD_ID] * (output_seq_len - len(sample[1]) - 1))

    encoder_inputs = []
    decoder_inputs = []
    target_weights = []

    for length_idx in range(input_seq_len):
        encoder_inputs.append(np.array([encoder_input[length_idx] for encoder_input in raw_encoder_input], dtype=np.int32))   # dtype是RNN状态数据的类型，默认是tf.float32
    for length_idx in range(output_seq_len):
        decoder_inputs.append(np.array([decoder_input[length_idx] for decoder_input in raw_decoder_input], dtype=np.int32))
        target_weights.append(np.array([
            0.0 if length_idx == output_seq_len - 1 or decoder_input[length_idx] == PAD_ID else 1.0 for decoder_input in raw_decoder_input
        ], dtype=np.float32))
    return encoder_inputs, decoder_inputs, target_weights


def seq_to_encoder(input_seq):
    """
    从输入空格分隔的数字id串，转成预测用的encoder、decoder、target_weight等
    """
    input_seq_array = [int(v) for v in input_seq.split()]
    encoder_input = [PAD_ID] * (input_seq_len - len(input_seq_array)) + input_seq_array
    decoder_input = [GO_ID] + [PAD_ID] * (output_seq_len - 1)
    encoder_inputs = [np.array([v], dtype=np.int32) for v in encoder_input]
    decoder_inputs = [np.array([v], dtype=np.int32) for v in decoder_input]
    target_weights = [np.array([1.0], dtype=np.float32)] * output_seq_len
    return encoder_inputs, decoder_inputs, target_weights


def get_model(feed_previous=False):
    """
    构造模型
    """
    learning_rate = tf.Variable(float(init_learning_rate), trainable=False, dtype=tf.float32)
    learning_rate_decay_op = learning_rate.assign(learning_rate * 0.9)

    encoder_inputs = []
    decoder_inputs = []
    target_weights = []
    for i in range(input_seq_len):
        encoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="encoder{0}".format(i)))
    for i in range(output_seq_len + 1):
        decoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="decoder{0}".format(i)))
    for i in range(output_seq_len):
        target_weights.append(tf.placeholder(tf.float32, shape=[None], name="weight{0}".format(i)))

    # decoder_inputs左移一个时序作为targets
    targets = [decoder_inputs[i + 1] for i in range(output_seq_len)]

    cell = tf.contrib.rnn.BasicLSTMCell(size)

    # 这里输出的状态我们不需要
    outputs, _ = seq2seq.embedding_attention_seq2seq(
                        encoder_inputs,
                        decoder_inputs[:output_seq_len],
                        cell,
                        num_encoder_symbols=num_encoder_symbols,
                        num_decoder_symbols=num_decoder_symbols,
                        embedding_size=size,
                        output_projection=None,  # 是一个(W, B)结构的tuple，W是shape为[output_size x num_decoder_symbols]的weight矩阵，B是shape为[num_decoder_symbols]的偏置向量
                        feed_previous=feed_previous,
                        dtype=tf.float32)

    # 计算交叉熵损失
    loss = seq2seq.sequence_loss(outputs, targets, target_weights)
    # 梯度下降优化器
    opt = tf.train.GradientDescentOptimizer(learning_rate)
    # 优化目标：让loss最小化
    update = opt.apply_gradients(opt.compute_gradients(loss))
    # 模型持久化,保存所有的变量
    saver = tf.train.Saver(tf.global_variables())

    return encoder_inputs, decoder_inputs, target_weights, outputs, loss, update, saver, learning_rate_decay_op, learning_rate


def train():
    """
    训练过程
    """
    train_set = get_train_set()
    with tf.Session() as sess:
        encoder_inputs, decoder_inputs, target_weights, outputs, loss, update, saver, learning_rate_decay_op, learning_rate = get_model()
        sess.run(tf.global_variables_initializer())

        # 训练很多次迭代，每隔100次打印一次loss，可以看情况直接ctrl+c停止
        previous_losses = []
        for step in range(epochs):  # 迭代次数
            sample_encoder_inputs, sample_decoder_inputs, sample_target_weights = get_samples(train_set, batch_num)
            input_feed = {}
            for l in range(input_seq_len):
                input_feed[encoder_inputs[l].name] = sample_encoder_inputs[l]
            for l in range(output_seq_len):
                input_feed[decoder_inputs[l].name] = sample_decoder_inputs[l]
                input_feed[target_weights[l].name] = sample_target_weights[l]
            # GO_ID需要去了
            input_feed[decoder_inputs[output_seq_len].name] = np.zeros([len(sample_decoder_inputs[0])], dtype=np.int32)
            [loss_ret, _] = sess.run([loss, update], input_feed)   # 开始对模型进行优化，即减小loss
            if step % 100 == 0:
                print('step=', step, 'loss=', loss_ret, 'learning_rate=', learning_rate.eval())  # 返回计算结果

                if len(previous_losses) > 5 and loss_ret > max(previous_losses[-5:]):
                    sess.run(learning_rate_decay_op)
                previous_losses.append(loss_ret)

                # 模型参数保存
                saver.save(sess, 'C:/Code/KnowledgeGraph-QA-master/model/')

train()