1.Word2Vec简介
Word2Vec也称Word Embeddings，中文比较常见的叫法是“词向量”或者是“词嵌入”。通俗的来说就是把单词进行编码，变成数字的形式让计算机知道那个单词的代号。哈哈， 感觉就像以前的间谍通过莫尔斯电码进行信息传递，只不过那个是通过声音的长短进行编码，我们如果使用one-hot的编码方式，比如I是第一个单词，那么在维度为10的单词向量中，编码就是[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]。通过这种方式我们进行编码，可以发现如果把一片文章进行编码，那么肯定是非常稀疏的，因为一个单词对应的向量只有一个有效的位置。而且还会存在一个问题，通常我们对单词的编码都是随机的，那么如果以“江苏”和“南京”为例，那么那样的编码关系不会存在地理联系，即江苏的省会城市是南京，这种包含关系是没办法体现的。 使用向量表达就可以有效地解决这个问题。它可以从原始语料中学习字词空间向量的预测模型。主要分为CBOW（Continuous Bag of Words）和Skip-Gram两种模式。在本篇博客中，我们主要使用的是Skip-Gram的模式。
2.Word2Vec的代码实现
#coding:utf-8 #因为要下载数据，所以导入的依赖库比较多 import collections import math import os import random import zipfile import numpy as np import sys import tensorflow as tf from sklearn.manifold import TSNE import matplotlib.pyplot as plt #这边是python版本的一个检查，不同版本对应函数调用的接口是不一样的 if sys.version_info[0] >= 3: from urllib.request import urlretrieve else: from urllib import urlretrieve #从网址下载数据并检查数据的准确性 url = 'http://mattmahoney.net/dc/' def maybe_download(filename, excepted_bytes): if not os.path.exists(filename): filename, _ = urlretrieve(url + filename, filename) statinfo = os.stat(filename) if statinfo.st_size == excepted_bytes: print("Found and verified", filename) else: print(statinfo.st_size) raise Exception( "Failed to verfy" + filename + "Can you get to it with browser?") return filename filename = maybe_download('text8.zip', 31344016) #定义读取数据的函数，并把数据转成列表 def read_data(filename): with zipfile.ZipFile(filename) as f: data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data words = read_data(filename) print('Data size', len(words)) #创建词汇表，选取前50000频数的单词，其余单词认定为Unknown,编号为0 vocabulary_size = 50000 def build_dataset(words): count = [['UNK', -1]] count.extend(collections.Counter(words).most_common(vocabulary_size - 1)) dictionary = dict() for word, _ in count: dictionary[word] = len(dictionary) data = list() unk_count = 0 for word in words: if word in dictionary: index = dictionary[word] else: index = 0 unk_count += 1 data.append(index) count[0][1] = unk_count reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) return data, count, dictionary, reverse_dictionary data, count, dictionary, reverse_dictionary = build_dataset(words) #为了节约内存删除原始单词列表，打印出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]]) #生成训练样本，assert断言：申明其布尔值必须为真的判定，如果发生异常，就表示为假 data_index = 0 def generate_batch(batch_size, num_skips, skip_window): 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) for i in range(batch_size // num_skips): target = skip_window targets_to_avoid = [skip_window] for j in range(num_skips): while target in targets_to_avoid: 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) return batch, labels #调用generate_batch函数简单测试一下功能 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]]) #定义训练是的参数 batch_size = 128 embedding_size = 128 skip_window = 1 num_skips = 2 valid_size = 16 valid_window = 100 valid_examples = np.random.choice(valid_window, valid_size, replace = False) num_sampled = 64 #定义Skip-Gram Word2Vec模型的网络结构 graph = tf.Graph() with graph.as_default(): 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) with tf.device('/gpu:0'): embeddings = tf.Variable( tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) 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(1.0).minimize(loss) norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims = True)) normalized_embeddings = embeddings / norm valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset) similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b = True) init = tf.global_variables_initializer() #定义最大迭代次数，创建并设置默认的session num_steps = 100001 with tf.Session(graph = graph) as session: init.run() print("Initialized") average_loss = 0 for step in range(num_steps): batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window) feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels} _, loss_val = session.run([optimizer, loss], feed_dict = feed_dict) average_loss += loss_val if step % 2000 == 0: if step > 0: average_loss /= 2000 print("Average loss at step ", step, ":", average_loss) average_loss = 0 if step % 10000 == 0: sim = similarity.eval() for i in range(valid_size): valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 nearest = (-sim[i, :]).argsort()[1: top_k+1] log_str = "Nearest to %s:" % valid_word for k in range(top_k): close_woreverse_dictionary[nearest[k]] log_str = "%s %s," %(log_str, close_word) print(log_str) final_embeddings = normalized_embeddings.eval() #定义可视化Word2Vec效果的函数 def plot_with_labels(low_dim_embs, labels, filename = 'tsne.png'): assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings" plt.figure(figsize= (18, 18)) for i, label in enumerate(labels): x, y = low_dim_embs[i, :] plt.scatter(x, y) plt.annotate(label, xy = (x, y), xy= (5, 2), textcoords = 'offset points', ha = 'right', va = 'bottom') plt.savefig(filename) tsne = TSNE(perplexity = 30, n_components = 2, init = 'pca', n_iter = 5000) plo t_only = 100 low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :]) labels = [reverse_dictionary[i] for i in range(plot_only)] plot_with_labels(low_dim_embs, labels)LZ其实对自然语言处理也不是很懂，所以NLP的部分也就只能浅尝辄止啦O(∩_∩)O
