tf11_01使用tf训练word2vec

分为4个步骤:
1)下载数据文件。即语料库。
2)处理语料库:把语料库转化为数据字典。选取语料库中频率最高的前49999个word,建立索引1:49999,其它word标为UNK(unknown),索引值为0。
3)为skip-gram model 准备数据。即一个input(target)需要有2*skip_window个output(上下文词汇)


tf11_01使用tf训练word2vec_第1张图片
图片.png

4)建立skip-gram model graph
5)training skip-gram model
6)可视化
tf code 如下:

import tensorflow as tf
# encoding=utf8  
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
import zipfile

import numpy as np
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
# Step 1: Download the data.
url = 'http://mattmahoney.net/dc/'

# 下载数据集
def maybe_download(filename, expected_bytes):
    """Download a file if not present, and make sure it's the right size."""
    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)

# Read the data into a list of strings.
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

# 单词表
words = read_data(filename)

# Data size
print('Data size', len(words))

Found and verified text8.zip
Data size 17005207

# Step 2: Build the dictionary and replace rare words with UNK token.
# 只留50000个单词,其他的词都归为UNK
vocabulary_size = 50000

def build_dataset(words, vocabulary_size):
    count = [['UNK', -1]]
    # extend追加一个列表
    # Counter用来统计每个词出现的次数
    # most_common返回一个TopN列表,只留50000个单词包括UNK  
    # c = Counter('abracadabra')
    # c.most_common()
    # [('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  # dictionary['UNK']
            unk_count += 1
        data.append(index)
    # 记录UNK词的数量
    count[0][1] = unk_count
    # 编号对应词的字典:把字典的键和值对换位置
    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)
del words  # Hint to reduce memory.
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 [5236, 3083, 12, 6, 195, 2, 3136, 46, 59, 156] ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']

# Step 3: Function to generate a training batch for the skip-gram model.
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  # [ skip_window target skip_window ]
    #deque:双向队列
    buffer = collections.deque(maxlen=span)
    # [ skip_window target skip_window ]
            # [ skip_window target skip_window ]
                    # [ skip_window target skip_window ]
            
#     [0 1 2 3 4 5 6 7 8 9 ...]
#            t     i  
    # 循环3次
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    # 获取batch和labels
    for i in range(batch_size // num_skips):
        target = skip_window  # target label at the center of the buffer
        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)#这里是从0,1,2中随机取一个,它包含两端的值,注意和np.random.randint的区别
            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)
    # Backtrack a little bit to avoid skipping words in the end of a batch
    # 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]])

3083 originated -> 5236 anarchism
3083 originated -> 12 as
12 as -> 6 a
12 as -> 3083 originated
6 a -> 12 as
6 a -> 195 term
195 term -> 6 a
195 term -> 2 of

# Step 4: Build and train a skip-gram model.
batch_size = 128
# 词向量维度
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。 
'''
注:num_skips = skip_window*2。这是必然的关系。如对应一段文本 a b c d e
如果skip_window = 1,num_skips = 2 对于c生成如下的上下文字符串
c -> b
c -> d
如果skip_window = 2,num_skips = 4 对于c生成如下的上下文字符串
c -> b
c -> d
c -> a
c -> e
注意这些字符串是没有先后顺序的。所以从这点而言,该模型已经遗漏了相对位置信息,有改进的空间。
'''


# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16     # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
# 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    # Number of negative examples to sample.

graph = tf.Graph()
with graph.as_default():
    # Input data.
    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)

    # Ops and variables pinned to the CPU because of missing GPU implementation
#     with tf.device('/cpu:0'):
        # 词向量
        # Look up embeddings for inputs.
    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)

    # Construct the variables for the noise-contrastive estimation(NCE) loss
    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]))

    # Compute the average NCE loss for the batch.
    # tf.nce_loss automatically draws a new sample of the negative labels each
    # time we evaluate the loss.
    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))

    # Construct the SGD optimizer using a learning rate of 1.0.
    optimizer = tf.train.GradientDescentOptimizer(1).minimize(loss)

    # Compute the cosine similarity(余弦相似度) between minibatch examples and all embeddings.
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    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)

    # Add variable initializer.
    init = tf.global_variables_initializer()
# Step 5: Begin training.
num_steps = 100001
final_embeddings = []

with tf.Session(graph=graph) as session:
    # We must initialize all variables before we use them.
    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_window)
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

        # We perform one update step by evaluating the optimizer op (including it
        # in the list of returned values for session.run()
        _, 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
            # The average loss is an estimate of the loss over the last 2000 batches.
            print("Average loss at step ", step, ": ", average_loss)
            average_loss = 0
    
        # Note that this is expensive (~20% slowdown if computed every 500 steps)
        if step % 20000 == 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  # number of nearest neighbors
                # 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 : 272.700042725
Nearest to than: segal, disgusting, vesicle, malaysians, cipher, plantinga, librarians, beach,
Nearest to there: tuscan, rather, pharsalus, willed, buckingham, handler, salzburg, expanse,
Nearest to no: pipelines, intuitions, burglar, coms, schrader, scalar, chlorine, restrained,
Nearest to a: neko, unmop, drama, madeline, quetzalcoatl, virginian, anschluss, fogo,
Nearest to was: fought, lp, museums, roster, assistant, penitent, hindus, ricardian,
Nearest to and: frail, migratory, rims, jewellery, outsold, rampant, tranquillity, parallax,
Nearest to to: ofcom, fortaleza, mast, libre, forums, bloodshed, vampyre, mummification,
Nearest to their: su, decided, magma, dong, spellings, contrapuntal, ulm, troglodytes,
Nearest to is: expects, dragged, slaughters, taipa, howitzer, conjugal, guessed, paran,
Nearest to i: predisposition, cpr, denard, desktops, crocodile, renewal, regatta, lama,
Nearest to years: amigaos, samplers, toolbars, annexing, joysticks, heraldic, unrecognized, epileptic,
Nearest to these: expressionism, pipes, neighbouring, acheson, superstition, drivetrain, harper, hot,
Nearest to often: status, evangelion, conjoined, declension, scooby, somatic, lpc, despot,
Nearest to however: marquesas, she, ensuring, knuth, footy, ciphertext, loew, workout,
Nearest to new: consultants, mohs, altruism, frasier, hybrid, biking, intrigue, quantity,
Nearest to has: cyanide, amal, frontispiece, stabilization, chad, beet, milestones, orpheus,
Average loss at step 2000 : 113.445801137
Average loss at step 4000 : 52.9759702828
Average loss at step 6000 : 33.313842569
Average loss at step 8000 : 23.3657903601
Average loss at step 10000 : 17.8983022878
Average loss at step 12000 : 14.2972284861
Average loss at step 14000 : 11.6379432209
Average loss at step 16000 : 9.91554711998
Average loss at step 18000 : 8.48712806338
Average loss at step 20000 : 7.9150552063
Nearest to than: nine, or, beach, vesicle, gnat, cipher, estimated, shaded,
Nearest to there: it, also, tuscan, frank, then, salzburg, rather, fathoms,
Nearest to no: moor, nitroglycerin, pipelines, msg, three, schopenhauer, and, phi,
Nearest to a: the, this, circ, agouti, his, rita, and, collectivist,
Nearest to was: is, by, were, as, are, agouti, and, has,
Nearest to and: in, or, of, mya, circ, UNK, agouti, dasyprocta,
Nearest to to: and, nine, for, picks, saints, in, with, traces,
Nearest to their: the, his, agouti, its, levites, a, an, machined,
Nearest to is: was, are, agouti, by, altenberg, has, were, as,
Nearest to i: vaccines, predisposition, gesta, crocodile, four, marx, renewal, advantage,
Nearest to years: gum, year, circ, annexing, dasyprocta, eight, mafia, agouti,
Nearest to these: pipes, content, reaching, suffixes, jude, operatorname, amazons, the,
Nearest to often: status, psi, somatic, agouti, tones, questionable, adelaide, norway,
Nearest to however: she, agouti, seminar, burroughs, and, footy, krebs, coke,
Nearest to new: backslash, quantity, altruism, park, poetic, archie, circ, hybrid,
Nearest to has: is, was, had, agouti, hitchcock, and, flagella, milestones,
……
Average loss at step 82000 : 4.77714890552
Average loss at step 84000 : 4.76479504359
Average loss at step 86000 : 4.76726497281
Average loss at step 88000 : 4.75223901606
Average loss at step 90000 : 4.74017631125
Average loss at step 92000 : 4.65827371514
Average loss at step 94000 : 4.73972713518
Average loss at step 96000 : 4.68014509404
Average loss at step 98000 : 4.58637025118
Average loss at step 100000 : 4.70240500081
Nearest to than: or, much, albury, cartographers, adria, cebus, mla, vesicle,
Nearest to there: they, it, he, albury, she, kapoor, mtsho, now,
Nearest to no: eucalyptus, albury, pipelines, connection, thaler, probably, nine, it,
Nearest to a: the, thaler, circ, ursus, iit, any, agouti, hanlon,
Nearest to was: is, became, had, has, were, thaler, by, being,
Nearest to and: or, but, ursus, cebus, while, dasyprocta, circ, abet,
Nearest to to: ursus, joram, could, coke, would, nine, abet, can,
Nearest to their: its, his, the, her, some, globemaster, agouti, thaler,
Nearest to is: was, has, are, became, agouti, thaler, be, does,
Nearest to i: you, ii, g, lama, thaler, he, gesta, we,
Nearest to years: year, annexing, sept, sunrise, seven, days, peacocks, wct,
Nearest to these: some, many, such, several, all, they, those, jude,
Nearest to often: widely, sometimes, usually, generally, also, commonly, wct, pulau,
Nearest to however: but, when, thaler, kapoor, ursus, although, that, cebus,
Nearest to new: backslash, microcebus, archie, ursus, kifl, circ, callithrix, agouti,
Nearest to has: had, have, is, was, agouti, thaler, circ, microcebus,

# 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))  # in inches
    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.")
tf11_01使用tf训练word2vec_第2张图片
tsne.png

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