word2vector代码实践
引子
在上次的 《word2vector论文笔记》中大致介绍了两种词向量训练方法的原理及优劣，这篇咱们以skip-gram算法为例来代码实践一把。
当前教程参考：A Word2Vec Keras tutorial
导库
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Input, Dense, Reshape, Dot, Embedding
from tensorflow.keras.losses import cosine_similarity
from tensorflow.keras.preprocessing.sequence import skipgrams
from tensorflow.keras.preprocessing import sequence
import urllib
import collections
import os
import zipfile
import numpy as np
import tensorflow as tf
tf.__version__
'2.0.0'
数据下载与预处理
将数据下载到本地，若本地已有数据则根据文件大小判断文件是否正确
def maybe_download(filename, url, expected_bytes):
"""Download a file if not present, and make sure it's the right size."""
if not os.path.exists(filename):
filename, _ = urllib.urlretrieve(url + filename, filename)
statinfo = os.stat(filename)
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
读取本地数据，输出为一个单词列表
def read_data(filename):
"""Extract the first file enclosed in a zip file as a list of words."""
with zipfile.ZipFile(filename) as f:
data = tf.compat.as_str(f.read(f.namelist()[0])).split()
return data
构造数据集
输入：单词列表、词典大小
输出：转为int后的单词列表、词频统计表、word2index字典、index2word字典
def build_dataset(words, n_words):
"""Process raw inputs into a dataset."""
count = [['UNK', -1]]
count.extend(collections.Counter(words).most_common(n_words - 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 # dictionary['UNK']
unk_count += 1
data.append(index)
count[0][1] = unk_count
reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
return data, count, dictionary, reversed_dictionary
借助以上几个功能函数，构造我们需要的数据集。并打印查看案例
def collect_data(vocabulary_size=10000):
url = 'http://mattmahoney.net/dc/'
filename = maybe_download('text8.zip', url, 31344016)
vocabulary = read_data(filename)
print(vocabulary[:7])
data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,
vocabulary_size)
del vocabulary # Hint to reduce memory.
return data, count, dictionary, reverse_dictionary
vocab_size = 10000
data, count, dictionary, reverse_dictionary = collect_data(vocabulary_size=vocab_size)
print(data[:7])
Found and verified text8.zip
['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse']
[5234, 3081, 12, 6, 195, 2, 3134]
构建模型
参数设定
window_size: 即每次sample样本的时候以当前词为中心向左右取词时的窗口大小。举例当前词（target）为Wi，则上下文词（context）为Wi-3,Wi-2,Wi-1,Wi+1,Wi+2,Wi+3
vector_dim：词向量的大小
epochs：模型要训练的轮数，这个有些夸张。论文中通常训练一轮或两轮下来模型收敛效果就已经很不错。跟语料规模也有很大关系。
window_size = 3
vector_dim = 300
epochs = 100000
valid_size = 16 # Random set of words to evaluate similarity on.
valid_window = 100 # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)
sequence.make_sampling_table函数用于构造一个根据词频进行抽样的抽样分布，不同词的抽样概率按照以下公式进行计算。此分布用于下一步生成skip-gram特定的样本。
P_(word) = min(1, \frac {sqrt{\frac {wordfrequency} {samplingfactor} }} {\frac {wordfrequency} {samplingfactor}})
sampling_table = sequence.make_sampling_table(vocab_size)
skipgrams 是tf提供的用于专门构造skip-gram训练样本的工具函数，只需要提供单词序列、词典大小、窗口大小、抽样表即可。如以下示例，couples对象第一个元素为target词，第二个为context词，labels表示当前词对是否处于同一条语料的指定窗口大小范围内。
couples, labels = skipgrams(data, vocab_size, window_size=window_size, sampling_table=sampling_table)
word_target, word_context = zip(*couples)
word_target = np.array(word_target, dtype="int32")
word_context = np.array(word_context, dtype="int32")
print(couples[:10], labels[:10])
[[5964, 1], [99, 379], [1385, 700], [4770, 1991], [3118, 9262], [4488, 6982], [4708, 6615], [9269, 7965], [4374, 5294], [4236, 6354]] [1, 1, 0, 1, 0, 0, 0, 0, 0, 0]
到此，我们用于模型训练的数据已经准备好啦！
关于softmax和负采样
我们看softmax的公式，为了做概率归一化，每次都需要针对词典中每个词计算其e为底的指数计算，这样当词典中的次数较大时计算成本是比较高昂的。
也有其他工作采用hierarchical softmax做为输出层。
skip-gram 则为此提出了下采样的二分类训练方法。
简言之就是根据窗口大小构造许多单词对，有些单词对确实是在目标词窗口范围内有共现的，称为正样本；反之，也根据不同词出现的频率对其他
非上下文的词进行采样，与目标词构成负样本单词对。我们的模型仅仅需要二分类就好。
创建模型
模型构建
模型本身很简单
把两个词分别输入，经过embedding层lookup到各自的词向量，通过向量点乘来衡量相似度，最后接一个单神经元的全连接层，通过sigmoid对输出进行激活，输出就是当前词对是否共现的概率判断，处于0-1之间。
损失函数为二分类损失binary_crossentropy
优化器选择rmsprop
# create some input variables
input_target = Input((1,))
input_context = Input((1,))
embedding = Embedding(vocab_size, vector_dim, input_length=1, name='embedding')
target = embedding(input_target)
target = Reshape((vector_dim, 1))(target)
context = embedding(input_context)
context = Reshape((vector_dim, 1))(context)
# now perform the dot product operation to get a similarity measure
dot_product = Dot(axes=1)([target, context])
dot_product = Reshape((1,))(dot_product)
# add the sigmoid output layer
output = Dense(1, activation='sigmoid')(dot_product)
# create the primary training model
model = Model(inputs=[input_target, input_context], outputs=output)
optimizer = tf.keras.optimizers.Adam(0.001)
model.compile(loss='binary_crossentropy', optimizer=optimizer)
模型检查
打印模型结构并检查，与我们预期一致。
共300W可训练参数
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
embedding (Embedding) (None, 1, 300) 3000000 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (None, 300, 1) 0 embedding[0][0]
__________________________________________________________________________________________________
reshape_1 (Reshape) (None, 300, 1) 0 embedding[1][0]
__________________________________________________________________________________________________
dot (Dot) (None, 1, 1) 0 reshape[0][0]
reshape_1[0][0]
__________________________________________________________________________________________________
reshape_2 (Reshape) (None, 1) 0 dot[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 1) 2 reshape_2[0][0]
==================================================================================================
Total params: 3,000,002
Trainable params: 3,000,002
Non-trainable params: 0
__________________________________________________________________________________________________
查找相似词，用于验证词向量训练效果
这里构造一个相似词查找回调类，它会针对最常见的几个词，寻找当前词向量训练结果下与其相似度最高的top_k个词。
class SimilarityCallback:
def run_sim(self):
for i in range(valid_size):
valid_word = reverse_dictionary[valid_examples[i]]
top_k = 8 # number of nearest neighbors
sim = self._get_sim(valid_examples[i])
nearest = (-sim).argsort()[1:top_k + 1]
log_str = 'Nearest to %s:' % valid_word
for k in range(top_k):
close_word = reverse_dictionary[nearest[k]]
log_str = '%s %s,' % (log_str, close_word)
print(log_str)
@staticmethod
def _get_sim(valid_word_idx):
sim = np.zeros((vocab_size,))
in_arr1 = np.zeros((1,))
in_arr2 = np.zeros((1,))
in_arr1[0,] = valid_word_idx
for i in range(vocab_size):
in_arr2[0,] = i
out = cosine_similarity(in_arr1, in_arr2)
sim[i] = out
return sim
sim_cb = SimilarityCallback()
训练并验证模型效果
每过1000个batch打印一次loss
每过10000个batch打印一次相似词查找结果
可以看到刚开始时由于模型权重随机初始化，针对高频词找出的相似词基本都没什么意义。
经过几万个batch的训练，找出的相似词已经与相应的高频词具有较强的相关性了。
arr_1 = np.zeros((1,))
arr_2 = np.zeros((1,))
arr_3 = np.zeros((1,))
for cnt in range(epochs):
idx = np.random.randint(0, len(labels)-1)
arr_1[0,] = word_target[idx]
arr_2[0,] = word_context[idx]
arr_3[0,] = labels[idx]
loss = model.train_on_batch([arr_1, arr_2], arr_3)
if cnt % 1000 == 0:
print("Iteration {}, loss={}".format(cnt, loss))
if cnt % 10000 == 0:
sim_cb.run_sim()
