前言

最近OpenAI的ChatGPT火爆全球，ChatGPT这款AI工具给我们展现了强大的生产力和创造力，人工智能是一次产业革命，将深刻影响我们的接下来的世界。ChatGPT强大的接近人类的对话能力离不开人类对其语言对话能力的训练。用向量表示文本中的词汇(或字符)是现代机器学习中最流行的做法, 这些向量能够很好的捕捉语言之间的关系, 从而提升基于词向量的各种NLP任务的效果。今天我们就用tensorflow训练词向量。

代码如下：

# -*- coding: utf-8 -*-
 
import time
import numpy as np
import tensorflow as tf
import random
from collections import Counter
 
# 2加载数据
# 
with open('data/Javasplittedwords',encoding='utf-8') as f:
    text = f.read()
 
# 3 数据预处理
# 3.1筛选低频词
words = text.split(' ')
words_count = Counter(words)
words = [w for w in words if words_count[w] > 50]
 
# 3.2构建映射表
vocab = set(words)
vocab_to_int = {w: c for c, w in enumerate(vocab)}
int_to_vocab = {c: w for c, w in enumerate(vocab)}
 
print("total words: {}".format(len(words)))
print("unique words: {}".format(len(set(words))))
 
# 3.3对原文本进行vocab到int的转换
int_words = [vocab_to_int[w] for w in words]# 4采样
# 对停用词进行采样，例如“the”，“of”以及“for”这类单词进行剔除。
# 剔除这些单词以后能够加快我们的训练过程，同时减少训练过程中的噪音。
t = 1e-5 # t值
threshold = 0.9 # 剔除概率阈值
 
# 统计单词出现频次
int_word_counts = Counter(int_words)
total_count = len(int_words)
# 计算单词频率
word_freqs = {w: c/total_count for w, c in int_word_counts.items()}
# 计算被删除的概率
prob_drop = {w: 1 - np.sqrt(t / word_freqs[w]) for w in int_word_counts}
# 对单词进行采样
train_words = [w for w in int_words if prob_drop[w] < threshold]#5 构造batch
def get_targets(words, idx, window_size=5):
    '''
    获得input word的上下文单词列表
    参数
    ---
    words: 单词列表
    idx: input word的索引号
    window_size: 窗口大小
    '''
    target_window = np.random.randint(1, window_size+1)
    # 这里要考虑input word前面单词不够的情况
    start_point = idx - target_window if (idx - target_window) > 0 else 0
    end_point = idx + target_window
    # output words(即窗口中的上下文单词)
    targets = set(words[start_point: idx] + words[idx+1: end_point+1])
    return list(targets)
 
def get_batches(words, batch_size, window_size=5):
    '''
    构造一个获取batch的生成器
    '''
    n_batches = len(words) // batch_size
     
    # 仅取full batches
    words = words[:n_batches*batch_size]
     
    for idx in range(0, len(words), batch_size):
        x, y = [], []
        batch = words[idx: idx+batch_size]
        for i in range(len(batch)):
            batch_x = batch[i]
            batch_y = get_targets(batch, i, window_size)
            # 由于一个input word会对应多个output word，因此需要长度统一
            x.extend([batch_x]*len(batch_y))
            y.extend(batch_y)
        yield x, y
         
# 6构建网络
# 6.1 输入层
# 嵌入矩阵的矩阵形状为  vocab_size×hidden_units_sizevocab_size×hidden_units_size 
train_graph = tf.Graph()
with train_graph.as_default():
    inputs = tf.placeholder(tf.int32, shape=[None], name='inputs')
    labels = tf.placeholder(tf.int32, shape=[None, None], name='labels')
     
 
#  6.2嵌入
vocab_size = len(int_to_vocab)
embedding_size = 200 # 嵌入维度
with train_graph.as_default():
    # 嵌入层权重矩阵
    embedding = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1, 1))
    # 实现lookup
    embed = tf.nn.embedding_lookup(embedding, inputs)
     
# 6.3负采样
n_sampled = 100
with train_graph.as_default():
    softmax_w = tf.Variable(tf.truncated_normal([vocab_size, embedding_size], stddev=0.1))
    softmax_b = tf.Variable(tf.zeros(vocab_size))
     
    # 计算negative sampling下的损失
    loss = tf.nn.sampled_softmax_loss(softmax_w, softmax_b, labels, embed, n_sampled, vocab_size)
     
    cost = tf.reduce_mean(loss)
    optimizer = tf.train.AdamOptimizer().minimize(cost)
     
# 6.4用查看语义相近的词的方法来验证
with train_graph.as_default():
    # 随机挑选一些单词
    ## From Thushan Ganegedara's implementation
    valid_size = 7 # Random set of words to evaluate similarity on.
    valid_window = 100
     
    # pick 8 samples from (0,100) and (1000,1100) each ranges. lower id implies more frequent 
#    valid_examples = np.array(random.sample(range(valid_window), valid_size//2))
#    valid_examples = np.append(valid_examples, random.sample(range(1000,1000+valid_window), valid_size//2))
    valid_examples = [vocab_to_int['word'], 
                      vocab_to_int['ppt'], 
                      vocab_to_int['熟悉'],
                      vocab_to_int['java'], 
                      vocab_to_int['能力'], 
                      vocab_to_int['逻辑思维'],
                      vocab_to_int['了解']]
    valid_size = len(valid_examples)
    # 验证单词集
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
     
    # 计算每个词向量的模并进行单位化
    norm = tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keep_dims=True))
    normalized_embedding = embedding / norm
     
    # 查找验证单词的词向量
    valid_embedding = tf.nn.embedding_lookup(normalized_embedding, valid_dataset)
    # 计算余弦相似度
    similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))
     
# 6.5实际训练
epochs = 10 # 迭代轮数
batch_size = 1000 # batch大小
window_size = 10 # 窗口大小
 
with train_graph.as_default():
    saver = tf.train.Saver() # 文件存储
 
with tf.Session(graph=train_graph) as sess:
    iteration = 1
    loss = 0
    sess.run(tf.global_variables_initializer())
 
    for e in range(1, epochs+1):
        batches = get_batches(train_words, batch_size, window_size)
        start = time.time()
        #
        i=0
        for x, y in batches:
            i=i+1
            if i<2:
                print(x,y)
            else:
                break
             
            feed = {inputs: x,
                    labels: np.array(y)[:, None]}
            train_loss, _ = sess.run([cost, optimizer], feed_dict=feed)
             
            loss += train_loss
             
            if iteration % 100 == 0: 
                end = time.time()
                print("Epoch {}/{}".format(e, epochs),
                      "Iteration: {}".format(iteration),
                      "Avg. Training loss: {:.4f}".format(loss/100),
                      "{:.4f} sec/batch".format((end-start)/100))
                loss = 0
                start = time.time()
             
            # 计算相似的词
            if iteration % 1000 == 0:
                # 计算similarity
                sim = similarity.eval()
                for i in range(valid_size):
                    valid_word = int_to_vocab[valid_examples[i]]
                    top_k = 8 # 取最相似单词的前8个
                    nearest = (-sim[i, :]).argsort()[1:top_k+1]
                    log = 'Nearest to [%s]:' % valid_word
                    for k in range(top_k):
                        close_word = int_to_vocab[nearest[k]]
                        log = '%s %s,' % (log, close_word)
                    print(log)
             
            iteration += 1
             
    save_path = saver.save(sess, "checkpoints/text8.ckpt")
    embed_mat = sess.run(normalized_embedding)