word2vec之tensorflow（skip-gram）实现

关于word2vec的理解，推荐文章https://www.geek-share.com/detail/2741185340.html

代码参考https://github.com/eecrazy/word2vec_chinese_annotation

我在其基础上修改了错误的部分，并添加了一些注释。

代码在jupyter notebook下运行。

from __future__ import print_function #表示不管哪个python版本，使用最新的print语法
import collections
import math
import numpy as np
import random
import tensorflow as tf
import zipfile
from matplotlib import pylab
from sklearn.manifold import TSNE
%matplotlib inline

下载https://www.cnblogs.com/lunge-blog/p/text8.zip文件，这个文件包含了大量单词。官方地址为http://mattmahoney.net/dc/https://www.cnblogs.com/lunge-blog/p/text8.zip

filename='https://www.cnblogs.com/lunge-blog/p/text8.zip'
def read_data(filename):
"""Extract the first file enclosed in a zip file as a list of words"""
with zipfile.ZipFile(filename) as f:
#     里面只有一个文件text8，包含了多个单词
#     f.read返回字节，tf.compat.as_str将字节转为字符
#     data包含了所有单词
data = tf.compat.as_str(f.read(f.namelist()[0])).split()
return data

#words里面包含了所有的单词
words = read_data(filename)
print('Data size %d' % len(words))

创建正-反词典，并将单词转换为词典索引，这里词汇表取为50000，仍然有400000多的单词标记为unknown。

#词汇表大小
vocabulary_size = 50000

def build_dataset(words):
#     表示未知，即不在词汇表里的单词，注意这里用的是列表形式而非元组形式，因为后面未知的数量需要赋值
count = [['UNK', -1]]
count.extend(collections.Counter(words).most_common(vocabulary_size - 1))

#词-索引哈希
dictionary = dict()
for word, _ in count:
#     每增加一个-->len+1，索引从0开始
dictionary[word] = len(dictionary)

#用索引表示的整个text8文本
data = list()
unk_count = 0
for word in words:
if word in dictionary:
index = dictionary[word]
else:
index = 0  # dictionary['UNK']
unk_count = unk_count + 1
data.append(index)

count[0][1] = unk_count
# 索引-词哈希
reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
return data, count, dictionary, reverse_dictionary

data, count, dictionary, reverse_dictionary = build_dataset(words)
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10])
# 删除，减少内存
del words  # Hint to reduce memory.

生成batch的函数

data_index = 0

# num_skips表示在两侧窗口内总共取多少个词，数量可以小于2*skip_window
# span窗口为[ skip_window target skip_window ]
# num_skips=2*skip_window
def generate_batch(batch_size, num_skips, skip_window):
global data_index

#这里两个断言
assert batch_size % num_skips == 0
assert num_skips <= 2 * skip_window

#初始化batch和labels，都是整形
batch = np.ndarray(shape=(batch_size), dtype=np.int32)
labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) #注意labels的形状

span = 2 * skip_window + 1 # [ skip_window target skip_window ]
#buffer这个队列太有用了，不断地保存span个单词在里面，然后不断往后滑动，而且buffer[skip_window]就是中心词
buffer = collections.deque(maxlen=span)

for _ in range(span):
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)

#需要多少个中心词，因为一个target对应num_skips个的单词，即一个目标单词w在num_skips=2时形成2个样本(w,left_w),(w,right_w)
#     这样描述了目标单词w的上下文
center_words_count=batch_size // num_skips
for i in range(center_words_count):
#skip_window在buffer里正好是中心词所在位置
target = skip_window  # target label at the center of the buffer
targets_to_avoid = [ skip_window ]
for j in range(num_skips):
#     选取span窗口中不包含target的，且不包含已选过的
target=random.choice([i for i in range(0,span) if i not in targets_to_avoid])
targets_to_avoid.append(target)
#         batch中重复num_skips次
batch[i * num_skips + j] = buffer[skip_window]
#         同一个target对应num_skips个上下文单词
labels[i * num_skips + j, 0] = buffer[target]
#     buffer滑动一格
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)
return batch, labels

# 打印前8个单词
print('data:', [reverse_dictionary[di] for di in data[:10]])
for num_skips, skip_window in [(2, 1), (4, 2)]:
data_index = 0
batch, labels = generate_batch(batch_size=16, num_skips=num_skips, skip_window=skip_window)
print('\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))
print('    batch:', [reverse_dictionary[bi] for bi in batch])
print('    labels:', [reverse_dictionary[li] for li in labels.reshape(16)])

我这里打印的结果为：可以看到batch和label的关系为，一个target单词多次对应于其上下文的单词

data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']

with num_skips = 2 and skip_window = 1:
batch: ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term', 'of', 'of', 'abuse', 'abuse', 'first', 'first', 'used', 'used']
labels: ['as', 'anarchism', 'originated', 'a', 'term', 'as', 'of', 'a', 'term', 'abuse', 'of', 'first', 'abuse', 'used', 'against', 'first']

with num_skips = 4 and skip_window = 2:
batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a', 'term', 'term', 'term', 'term', 'of', 'of', 'of', 'of']
labels: ['anarchism', 'originated', 'a', 'term', 'originated', 'of', 'as', 'term', 'of', 'a', 'abuse', 'as', 'a', 'term', 'first', 'abuse']

构建model，定义loss：

batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.

valid_size = 16 # Random set of words to evaluate similarity on.
valid_window = 100 # Only pick dev samples in the head of the distribution.
#随机挑选一组单词作为验证集，valid_examples也就是下面的valid_dataset，是一个一维的ndarray
valid_examples = np.array(random.sample(range(valid_window), valid_size))

#trick：负采样数值
num_sampled = 64 # Number of negative examples to sample.

graph = tf.Graph()

with graph.as_default(), tf.device('/cpu:0'):

# 训练集和标签，以及验证集（注意验证集是一个常量集合）
train_dataset = 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)

# 定义Embedding层，初始化。
embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
softmax_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],stddev=1.0 / math.sqrt(embedding_size)))
softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))

# Model.
# train_dataset通过embeddings变为稠密向量，train_dataset是一个一维的ndarray
embed = tf.nn.embedding_lookup(embeddings, train_dataset)

# Compute the softmax loss, using a sample of the negative labels each time.
# 计算损失，tf.reduce_mean和tf.nn.sampled_softmax_loss
loss = tf.reduce_mean(tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=embed,
labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))

# Optimizer.优化器，这里也会优化embeddings
# Note: The optimizer will optimize the softmax_weights AND the embeddings.
# This is because the embeddings are defined as a variable quantity and the
# optimizer's `minimize` method will by default modify all variable quantities
# that contribute to the tensor it is passed.
# See docs on `tf.train.Optimizer.minimize()` for more details.
optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)

# 模型其实到这里就结束了，下面是在验证集上做效果验证
# Compute the similarity between minibatch examples and all embeddings.
# We use the cosine distance:先对embeddings做正则化
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)
#验证集单词与其他所有单词的相似度计算
similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))

开始训练：

num_steps = 40001
with tf.Session(graph=graph) as session:
tf.initialize_all_variables().run()
print('Initialized')
average_loss = 0
for step in range(num_steps):
batch_data, batch_labels = generate_batch(batch_size, num_skips, skip_window)
feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
_, this_loss = session.run([optimizer, loss], feed_dict=feed_dict)

average_loss += this_loss
#     每2000步计算一次平均loss
if step % 2000 == 0:
if step > 0:
average_loss = average_loss / 2000
# The average loss is an estimate of the loss over the last 2000 batches.
print('Average loss at step %d: %f' % (step, average_loss))
average_loss = 0

# note that this is expensive (~20% slowdown if computed every 500 steps)
if step % 10000 == 0:
sim = similarity.eval()
for i in range(valid_size):
valid_word = reverse_dictionary[valid_examples[i]]
top_k = 8 # number of nearest neighbors
#         nearest = (-sim[i, :]).argsort()[1:top_k+1]
nearest = (-sim[i, :]).argsort()[0:top_k+1]#包含自己试试
log = 'Nearest to %s:' % valid_word
for k in range(top_k):
close_word = reverse_dictionary[nearest[k]]
log = '%s %s,' % (log, close_word)
print(log)
#一直到训练结束，再对所有embeddings做一次正则化，得到最后的embedding
final_embeddings = normalized_embeddings.eval()

我们可以看下训练过程中的验证情况，比如many这个单词的相似词计算：

 开始时，

Nearest to many: many, originator, jeddah, maxwell, laurent, distress, interpret, bucharest,

10000步后，

Nearest to many: many, some, several, jeddah, originator, neurath, distress, songs,

40000步后，

Nearest to many: many, some, several, these, various, such, other, most,

可以看到此时单词的相似度确实很高了。

最后，我们通过降维，将单词相似情况以图示展现出来：

num_points = 400
# 降维度PCA
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])
def plot(embeddings, labels):
assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
pylab.figure(figsize=(15,15))  # in inches
for i, label in enumerate(labels):
x, y = embeddings[i,:]
pylab.scatter(x, y)
pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
ha='right', va='bottom')
pylab.show()

words = [reverse_dictionary[i] for i in range(1, num_points+1)]
plot(two_d_embeddings, words)

结果如下，随便举些例子，university和college相近，take和took相近，one、two、three等相近

总结：原始的word2vec是用c语言写的，这里用的python，结合的tensorflow。这个代码存在一些问题，首先，单词不是以索引作为输入的，应该是以one-hot形式输入。其次，负采样的比例太小，词汇表有50000，每批样本才选64个去做softmax。然后，这里也没使用到另一个trick（当然这里根本没用one-hot，这个trick也不存在了，我甚至觉得根本不需要负采样）：将单词构建为二叉树（类似于从one-hot维度降低到二叉树编码（如哈夫曼树）），从而实现一种降维操作。不过，即使是这个简陋的模型，效果看起来依然不错，即方向对了，醉汉也能走到家。