参考：《tensorflow实战》
首先介绍一下Word2Vec
Word2Vec：从原始语料中学习字词空间向量的预测模型。主要分为CBOW（Continue Bags of Words）连续词袋模型和Skip-Gram两种模式
CBOW：从原始语句（中国的首都是___）推测目标字词（北京）。Skip-Gram正好相反，从目标词反推原始语句。
预测模型使用最大似然的方法。在给定前面的语句h的情况下，最大化目标词汇的概率。比如（中国的___是北京），首都就是我们的目标词汇。
使用NCE（噪声对比估计 Noise-Contrastive Estimation）作为损失函数。
NCE：把上下文h对应的正确的词汇标记为正样本D=1，再抽取一些错误的词汇作为负样本（D=0），然后最大化目标函数的值
基于Skip-Gram的Word2Vec
import collections import math import os import random import zipfile import numpy as np import urllib import tensorflow as tf from six.moves import xrange # pylint: disable=redefined-builtin import matplotlib as plt
一、下载数据
下载完数据之后，当前文件夹下有text8.zip文件
# 下载数据 # 如何下载过了，就不需要执行这段代码 url = 'http://mattmahoney.net/dc/' def maybe_download(filename, expected_bytes): if not os.path.exists(filename): filename, _ = urllib.request.urlretrieve(url + filename, filename) # 获取文件相关属性 statinfo = os.stat(filename) # 比对文件的大小是否正确 if statinfo.st_size == expected_bytes: print('Found and verified', filename) else: print(statinfo.st_size) raise Exception('Failed to verify ' + filename + '. Can you get to it with a browser?') return filename filename = maybe_download('text8.zip', 31344016)
Found and verified text8.zip
二、解压下载的压缩文件
# 解压下载的压缩文件 def read_data(filename): with zipfile.ZipFile(filename) as f: data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data # 单词表 words = read_data(filename) print('Data size', len(words))
Data size 17005207
三、创建vocabulary词汇表，取top50000频数的单词
# 只留50000个单词，其他的词都归为UNK # UNK ： 不认识的词 vocabulary_size = 50000 def build_dataset(words, vocabulary_size): count = [['UNK', -1]] # extend追加一个列表 # Counter用来统计没个词出现的次数 # most_common返回一个Top列表，只留50000个单词包括UNK # c = Counter('abracadabra') # c.most_commom() # [('a', 5), ('r', 2), ('b', 2), ('c', 1), ('d', 1)] # c.most_common(3) # [('a', 5), ('r', 2), ('b', 2)] # 前50000个出现次数最多的词 count.extend(collections.Counter(words).most_common(vocabulary_size - 1)) # 生成 dictionary，词对应编号, word:id(0-49999) # 词频越高编号越小 dictionary = dict() for word, _ in count: dictionary[word] = len(dictionary) # data把数据集的词都编号 data = list() unk_count = 0 for word in words: if word in dictionary: index = dictionary[word] else: index = 0 unk_count += 1 # dictionary['UNK'] data.append(index) # 记录UNK词的数量 count[0][1] = unk_count # 编号对应词的字典 reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) return data, count, dictionary, reverse_dictionary
# data 数据集，编号形式 # count 前50000个出现次数最多的词 # dictionary 词对应编号 # reverse_dictionary 编号对应词 data, count, dictionary, reverse_dictionary = build_dataset(words, vocabulary_size)
# 删除原始单词列表，节约内存 # 打印最高频出现的词汇及其数量 del words print('Most common words (+UNK)', count[:5]) print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]
Sample data [5234, 3081, 12, 6, 195, 2, 3134, 46, 59, 156] ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']
四、生成Word2Vec的训练样本
1. 使用Skip-Gram模式（从目标单词反推语境）
data_index = 0 def generate_batch(batch_size, num_skips, skip_window): # skip_window ：单词最远可以联系的距离，设为1代表只能跟紧邻的两个单词生成样本 # num_skips：每个单词生成多少样本 # batch_size必须是num_skips的整数倍（确保每个batch包含了一个词汇对应的所有样本） global data_index assert batch_size % num_skips == 0 assert num_skips <= 2 * skip_window batch = np.ndarray(shape=(batch_size), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 buffer = collections.deque(maxlen=span) for _ in range(span): buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) # 获取batch和label for i in range(batch_size // num_skips): target = skip_window targets_to_avoid = [skip_window] # 循环2次，一个目标单词对应两个上下文单词 for j in range(num_skips): while target in targets_to_avoid: # 可能先拿到前面的单词也可能先拿到后面的单词 target = random.randint(0, span - 1) targets_to_avoid.append(target) batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[target] buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) # 回溯3个词。因为执行完一个batch的操作之后，data_index会往右多偏移span个位置 data_index = (data_index + len(data) - span) % len(data) return batch, labels
# 打印sample data batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1) for i in range(8): print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
3081 originated -> 5234 anarchism 3081 originated -> 12 as 12 as -> 6 a 12 as -> 3081 originated 6 a -> 195 term 6 a -> 12 as 195 term -> 2 of 195 term -> 6 a
五、建立和训练一个skip-gram模型
# 建立和训练一个skip-gram模型 batch_size = 128 # 词向量维度 embedding_size = 128 skip_windows = 1 num_skips = 2 valid_size = 16 valid_window = 100 # 从0-100抽取16个整数，无放回抽样 valid_examples = np.random.choice(valid_window, valid_size, replace=False) # 负采样样本数 num_sampled = 64
graph = tf.Graph() with graph.as_default(): # 输入数据 train_inputs = tf.placeholder(tf.int32, shape=[batch_size]) train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) valid_dataset = tf.constant(valid_examples, dtype=tf.int32) embeddings = tf.Variable( tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) # embedding_lookup(params,ids)其实就是按照ids顺序返回params中的第ids行 # 比如说，ids=[1,7,4],就是返回params中第1,7,4行。返回结果为由params的1,7,4行组成的tensor # 提取要训练的词 embed = tf.nn.embedding_lookup(embeddings, train_inputs) nce_weights = tf.Variable( tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size))) nce_biases = tf.Variable(tf.zeros([vocabulary_size])) loss = tf.reduce_mean( tf.nn.nce_loss(weights=nce_weights, biases=nce_biases, labels=train_labels, inputs=embed, num_sampled=num_sampled, num_classes=vocabulary_size)) optimizer = tf.train.GradientDescentOptimizer(1).minimize(loss) # embeddings 的L2范数 norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) # embeddings除以L2范数得到标准化后的 normalized_embeddings normalized_embeddings = embeddings / norm # 抽取一些常用词来测试余弦相似度 valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset) # valid_size == 16 # [16,1] * [1*50000] = [16,50000] similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True) init = tf.global_variables_initializer()
优化器为SGD，然后计算嵌入向量embedding的L2范数norm，在将embedding除以其L2范数得到标准化后的normalized_embeddings。使用tf.nn.embedding_lookup查询单词的嵌入向量，并计算验证单词的嵌入向量与词汇表中所有单词的相似性。
# Step 5: Begin training. num_steps = 100001 final_embeddings = [] with tf.Session(graph=graph) as session: init.run() print("Initialized") average_loss = 0 for step in xrange(num_steps): # 获取一个批次的target，以及对应的labels，都是编号形式的 batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_windows) feed_dict = {train_inputs:batch_inputs, train_labels:batch_labels} _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict) average_loss += loss_val # 计算训练2000次的平均loss if step % 2000 == 0: if step > 0: average_loss /= 2000 print("Average loss at step ", step, ": ", average_loss) average_loss = 0 if step & 2000 == 0: sim = similarity.eval() # 计算验证集的余弦相似度最高的词 for i in xrange(valid_size): # 根据id拿到对应单词 valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 # 从大到小排序，排除自己本身，取前top_k个值 nearest = (-sim[i, :]).argsort()[1:top_k + 1] log_str = "Nearest to %s:" % valid_word for k in xrange(top_k): close_word = reverse_dictionary[nearest[k]] log_str = "%s %s," % (log_str, close_word) print(log_str) # 训练结束得到的词向量 final_embeddings = normalized_embeddings.eval()
Initialized Average loss at step 0 : 296.63623046875 Nearest to b: game, ampersand, odour, reorganize, relented, missiles, svetlana, sustains,
......
六、可视化效果函数
# Step 6: Visualize the embeddings. def plot_with_labels(low_dim_embs, labels, filename='tsne.png'): assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings" # 设置图片大小 plt.figure(figsize = (15, 15)) for i, label in enumerate(labels): x, y = low_dim_embs[i, :] plt.scatter(x, y) plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom') plt.savefig(filename)
try: from sklearn.manifold import TSNE import matplotlib.pyplot as plt tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')# mac：method='exact' # 画500个点 plot_only = 500 low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :]) labels = [reverse_dictionary[i] for i in xrange(plot_only)] plot_with_labels(low_dim_embs, labels) except ImportError: print("Please install sklearn, matplotlib, and scipy to visualize embeddings."
最后生成的图片如下，效果还可以。距离相近的单词在语义上具有很高的相似度。
