使用TensorFlow v2.0实现Word2Vec算法计算单词的向量表示，这个例子是使用一小部分维基百科文章来训练的。

更多信息请查看论文: Mikolov, Tomas et al. “Efficient Estimation of Word Representations in Vector Space.”, 20131

from __future__ import division, print_function, absolute_import

import collections

import os

import random

import urllib

import zipfile

import numpy as np

import tensorflow as tf




learning_rate = 0.1

batch_size = 128

num_steps = 3000000

display_step = 10000

eval_step = 200000

# 训练参数

learning_rate = 0.1

batch_size = 128

num_steps = 3000000

display_step = 10000

eval_step = 200000

# 评估参数

eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain']

# Word2Vec 参数

embedding_size = 200 # 嵌入向量的维度 vector.

max_vocabulary_size = 50000 # 词汇表中不同单词的总数words in the vocabulary.

min_occurrence = 10  # 删除出现小于n次的所有单词

skip_window = 3 # 左右各要考虑多少个单词

num_skips = 2 # 重复使用输入生成标签的次数

num_sampled = 64 # 负采样数量


# 下载一小部分维基百科文章集

url = 'http://mattmahoney.net/dc/text8.zip'

data_path = 'text8.zip'

if not os.path.exists(data_path):

    print("Downloading the dataset... (It may take some time)")

    filename, _ = urllib.urlretrieve(url, data_path)

    print("Done!")

# 解压数据集文件，文本已处理完毕

with zipfile.ZipFile(data_path) as f:

    text_words = f.read(f.namelist()[0]).lower().split()


# 构建词典并用 UNK 标记替换频数较低的词

count = [('UNK', -1)]

# 检索最常见的单词

count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1))

# 删除少于'min_occurrence'次数的样本

for i in range(len(count) - 1, -1, -1):

    if count[i][1] < min_occurrence:

        count.pop(i)

    else:

        #该集合是有序的，因此在当出现小于'min_occurrence'时停止

        break

# 计算单词表单词个数

vocabulary_size = len(count)

# 为每一个词分配id

word2id = dict()

for i, (word, _)in enumerate(count):

    word2id[word] = i

data = list()

unk_count = 0

for word in text_words:

     # 检索单词id，或者如果不在字典中则为其指定索引0（'UNK'）

    index = word2id.get(word, 0)

    if index == 0:

        unk_count  = 1

    data.append(index)

count[0] = ('UNK', unk_count)

id2word = dict(zip(word2id.values(), word2id.keys()))

print("Words count:", len(text_words))

print("Unique words:", len(set(text_words)))

print("Vocabulary size:", vocabulary_size)

print("Most common words:", count[:10])

output:

Words count: 17005207

Unique words: 253854

Vocabulary size: 47135

Most common words: [('UNK', 444176), ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]


data_index = 0

# 为skip-gram模型生成训练批次

def next_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)

    # 得到窗口长度( 当前单词左边和右边   当前单词)

    span = 2 * skip_window   1

    buffer = collections.deque(maxlen=span)

    if data_index   span > len(data):

        data_index = 0

    buffer.extend(data[data_index:data_index   span])

    data_index  = span

    for i in range(batch_size // num_skips):

        context_words = [w for w in range(span) if w != skip_window]

        words_to_use = random.sample(context_words, num_skips)

        for j, context_word in enumerate(words_to_use):

            batch[i * num_skips   j] = buffer[skip_window]

            labels[i * num_skips   j, 0] = buffer[context_word]

        if data_index == len(data):

            buffer.extend(data[0:span])

            data_index = span

        else:

            buffer.append(data[data_index])

            data_index  = 1

    #回溯一点，以避免在批处理结束时跳过单词

    data_index = (data_index   len(data) - span) % len(data)

    return batch, labels


# 确保在CPU上分配以下操作和变量

# (某些操作在GPU上不兼容)

with tf.device('/cpu:0'):

    # 创建嵌入变量（每一行代表一个词嵌入向量） embedding vector).

    embedding = tf.Variable(tf.random.normal([vocabulary_size, embedding_size]))

    # 构造NCE损失的变量

    nce_weights = tf.Variable(tf.random.normal([vocabulary_size, embedding_size]))

    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

def get_embedding(x):

    with tf.device('/cpu:0'):

       # 对于X中的每一个样本查找对应的嵌入向量

        x_embed = tf.nn.embedding_lookup(embedding, x)

        return x_embed

def nce_loss(x_embed, y):

    with tf.device('/cpu:0'):

        # 计算批处理的平均NCE损失

        y = tf.cast(y, tf.int64)

        loss = tf.reduce_mean(

            tf.nn.nce_loss(weights=nce_weights,

                           biases=nce_biases,

                           labels=y,

                           inputs=x_embed,

                           num_sampled=num_sampled,

                           num_classes=vocabulary_size))

        return loss

# 评估

def evaluate(x_embed):

    with tf.device('/cpu:0'):

         # 计算输入数据嵌入与每个嵌入向量之间的余弦相似度

        x_embed = tf.cast(x_embed, tf.float32)

        x_embed_norm = x_embed / tf.sqrt(tf.reduce_sum(tf.square(x_embed)))

        embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True), tf.float32)

        cosine_sim_op = tf.matmul(x_embed_norm, embedding_norm, transpose_b=True)

        return cosine_sim_op

# 定义优化器

optimizer = tf.optimizers.SGD(learning_rate)


# 优化过程

def run_optimization(x, y):

    with tf.device('/cpu:0'):

       # 将计算封装在GradientTape中以实现自动微分

        with tf.GradientTape() as g:

            emb = get_embedding(x)

            loss = nce_loss(emb, y)

        # 计算梯度

        gradients = g.gradient(loss, [embedding, nce_weights, nce_biases])

         # 按gradients更新 W 和 b

        optimizer.apply_gradients(zip(gradients, [embedding, nce_weights, nce_biases]))


# 用于测试的单词

x_test = np.array([word2id[w] for w in eval_words])

# 针对给定步骤数进行训练

for step in xrange(1, num_steps   1):

    batch_x, batch_y = next_batch(batch_size, num_skips, skip_window)

    run_optimization(batch_x, batch_y)

    if step % display_step == 0 or step == 1:

        loss = nce_loss(get_embedding(batch_x), batch_y)

        print("step: %i, loss: %f" % (step, loss))

    # 评估

    if step % eval_step == 0 or step == 1:

        print("Evaluation...")

        sim = evaluate(get_embedding(x_test)).numpy()

        for i in xrange(len(eval_words)):

            top_k = 8  # 最相似的单词数量

            nearest = (-sim[i, :]).argsort()[1:top_k   1]

            log_str = '"%s" nearest neighbors:' % eval_words[i]

            for k in xrange(top_k):

                log_str = '%s %s,' % (log_str, id2word[nearest[k]])

            print(log_str)


step: 1, loss: 504.444214

Evaluation...

"five" nearest neighbors: censure, stricken, anglicanism, stick, streetcars, shrines, horrified, sparkle,

"of" nearest neighbors: jolly, weary, clinicians, kerouac, economist, owls, safe, playoff,

"going" nearest neighbors: filament, platforms, moderately, micheal, despotic, krag, disclosed, your,

"hardware" nearest neighbors: occupants, paraffin, vera, reorganized, rename, declares, prima, condoned,

"american" nearest neighbors: portfolio, rhein, aalto, angle, lifeson, tucker, sexton, dench,

"britain" nearest neighbors: indivisible, disbelief, scripture, pepsi, scriptores, sighting, napalm, strike,

step: 10000, loss: 117.166962

step: 20000, loss: 65.478333

step: 30000, loss: 46.580460

step: 40000, loss: 25.563128

step: 50000, loss: 50.924446

step: 60000, loss: 51.696526

step: 70000, loss: 17.272142

step: 80000, loss: 32.579414

step: 90000, loss: 68.372032

step: 100000, loss: 36.026573

step: 110000, loss: 22.502020

step: 120000, loss: 15.788742

step: 130000, loss: 31.832420

step: 140000, loss: 25.096617

step: 150000, loss: 12.013027

step: 160000, loss: 20.574780

step: 170000, loss: 12.201975

step: 180000, loss: 20.983793

step: 190000, loss: 11.366720

step: 200000, loss: 19.431549

Evaluation...

"five" nearest neighbors: three, four, eight, six, two, seven, nine, zero,

"of" nearest neighbors: the, a, and, first, with, on, but, from,

"going" nearest neighbors: have, more, used, out, be, with, on, however,

"hardware" nearest neighbors: be, known, system, apollo, and, a, such, used,

"american" nearest neighbors: UNK, and, from, s, at, in, after, about,

"britain" nearest neighbors: of, and, many, the, as, used, but, such,

step: 210000, loss: 16.361233

step: 220000, loss: 17.529526

step: 230000, loss: 16.805817

step: 240000, loss: 6.365625

step: 250000, loss: 8.083097

step: 260000, loss: 11.262514

step: 270000, loss: 9.842708

step: 280000, loss: 6.363440

step: 290000, loss: 8.732617

step: 300000, loss: 10.484728

step: 310000, loss: 12.099487

step: 320000, loss: 11.496288

step: 330000, loss: 9.283813

step: 340000, loss: 10.777218

step: 350000, loss: 16.310440

step: 360000, loss: 7.495782

step: 370000, loss: 9.287696

step: 380000, loss: 6.982735

step: 390000, loss: 8.549622

step: 400000, loss: 8.388112

Evaluation...

"five" nearest neighbors: four, three, six, two, seven, eight, one, zero,

"of" nearest neighbors: the, a, with, also, for, and, which, by,

"going" nearest neighbors: have, are, both, called, being, a, of, had,

"hardware" nearest neighbors: may, de, some, have, so, which, other, also,

"american" nearest neighbors: s, british, UNK, from, in, including, first, see,

"britain" nearest neighbors: against, include, including, both, british, other, an, most,

step: 410000, loss: 8.757725

step: 420000, loss: 12.303110

step: 430000, loss: 12.325478

step: 440000, loss: 7.659882

step: 450000, loss: 6.028089

step: 460000, loss: 12.700299

step: 470000, loss: 7.063077

step: 480000, loss: 18.004183

step: 490000, loss: 7.510474

step: 500000, loss: 10.089376

step: 510000, loss: 11.404436

step: 520000, loss: 9.494527

step: 530000, loss: 7.797963

step: 540000, loss: 7.390718

step: 550000, loss: 13.911215

step: 560000, loss: 6.975731

step: 570000, loss: 6.179163

step: 580000, loss: 7.066525

step: 590000, loss: 6.487288

step: 600000, loss: 5.361528

Evaluation...

"five" nearest neighbors: four, six, three, seven, two, one, eight, zero,

"of" nearest neighbors: the, and, from, with, a, including, in, include,

"going" nearest neighbors: have, even, they, term, who, many, which, were,

"hardware" nearest neighbors: include, computer, an, which, other, each, than, may,

"american" nearest neighbors: english, french, s, german, from, in, film, see,

"britain" nearest neighbors: several, first, modern, part, government, german, was, were,

step: 610000, loss: 4.144980

step: 620000, loss: 5.865635

step: 630000, loss: 6.826498

step: 640000, loss: 8.376097

step: 650000, loss: 7.117930

step: 660000, loss: 7.639544

step: 670000, loss: 5.973255

step: 680000, loss: 4.908459

step: 690000, loss: 6.164993

step: 700000, loss: 7.360281

step: 710000, loss: 12.693079

step: 720000, loss: 6.410182

step: 730000, loss: 7.499201

step: 740000, loss: 6.509094

step: 750000, loss: 10.625893

step: 760000, loss: 7.177696

step: 770000, loss: 12.639092

step: 780000, loss: 8.441635

step: 790000, loss: 7.529139

step: 800000, loss: 6.579177

Evaluation...

"five" nearest neighbors: four, three, six, seven, eight, two, one, zero,

"of" nearest neighbors: and, with, in, the, its, from, by, including,

"going" nearest neighbors: have, they, how, include, people, however, also, their,

"hardware" nearest neighbors: computer, large, include, may, or, which, other, there,

"american" nearest neighbors: born, french, british, english, german, b, john, d,

"britain" nearest neighbors: country, including, include, general, part, various, several, by,

step: 810000, loss: 6.934138

step: 820000, loss: 5.686094

step: 830000, loss: 7.310243

step: 840000, loss: 5.028157

step: 850000, loss: 7.079705

step: 860000, loss: 6.768996

step: 870000, loss: 5.604030

step: 880000, loss: 8.208309

step: 890000, loss: 6.301597

step: 900000, loss: 5.733234

step: 910000, loss: 6.577081

step: 920000, loss: 6.774826

step: 930000, loss: 7.068932

step: 940000, loss: 6.694956

step: 950000, loss: 7.944673

step: 960000, loss: 5.988618

step: 970000, loss: 6.651366

step: 980000, loss: 4.595577

step: 990000, loss: 6.564834

step: 1000000, loss: 4.327858

Evaluation...

"five" nearest neighbors: four, three, seven, six, eight, two, nine, zero,

"of" nearest neighbors: the, first, and, became, from, under, at, with,

"going" nearest neighbors: others, has, then, have, how, become, had, also,

"hardware" nearest neighbors: computer, large, systems, these, different, either, include, using,

"american" nearest neighbors: b, born, d, UNK, nine, english, german, french,

"britain" nearest neighbors: government, island, local, country, by, including, control, within,

step: 1010000, loss: 5.841236

step: 1020000, loss: 5.805200

step: 1030000, loss: 9.962063

step: 1040000, loss: 6.281199

step: 1050000, loss: 7.147995

step: 1060000, loss: 5.721184

step: 1070000, loss: 7.080662

step: 1080000, loss: 6.638658

step: 1090000, loss: 5.814178

step: 1100000, loss: 5.195928

step: 1110000, loss: 6.724787

step: 1120000, loss: 6.503905

step: 1130000, loss: 5.762966

step: 1140000, loss: 5.790243

step: 1150000, loss: 5.958191

step: 1160000, loss: 5.997983

step: 1170000, loss: 7.065348

step: 1180000, loss: 6.073387

step: 1190000, loss: 6.644097

step: 1200000, loss: 5.934450

Evaluation...

"five" nearest neighbors: three, four, six, eight, seven, two, nine, zero,

"of" nearest neighbors: the, and, including, in, its, with, from, on,

"going" nearest neighbors: others, then, through, has, had, another, people, when,

"hardware" nearest neighbors: computer, control, systems, either, these, large, small, other,

"american" nearest neighbors: born, german, john, d, british, b, UNK, french,

"britain" nearest neighbors: local, against, british, island, country, general, including, within,

step: 1210000, loss: 5.832344

step: 1220000, loss: 6.453851

step: 1230000, loss: 6.583966

step: 1240000, loss: 5.571673

step: 1250000, loss: 5.720917

step: 1260000, loss: 7.663424

step: 1270000, loss: 6.583741

step: 1280000, loss: 8.503859

step: 1290000, loss: 5.540640

step: 1300000, loss: 6.703249

step: 1310000, loss: 5.274101

step: 1320000, loss: 5.846446

step: 1330000, loss: 5.438172

step: 1340000, loss: 6.367691

step: 1350000, loss: 6.558622

step: 1360000, loss: 9.822924

step: 1370000, loss: 4.982378

step: 1380000, loss: 6.159739

step: 1390000, loss: 5.819083

step: 1400000, loss: 7.775135

Evaluation...

"five" nearest neighbors: four, three, six, seven, two, eight, one, zero,

"of" nearest neighbors: and, the, in, with, its, within, for, including,

"going" nearest neighbors: others, through, while, has, to, how, particularly, their,

"hardware" nearest neighbors: computer, systems, large, control, research, using, information, either,

"american" nearest neighbors: english, french, german, born, film, british, s, former,

"britain" nearest neighbors: british, country, europe, local, military, island, against, western,

step: 1410000, loss: 8.214248

step: 1420000, loss: 4.696859

step: 1430000, loss: 5.873761

step: 1440000, loss: 5.971557

step: 1450000, loss: 4.992722

step: 1460000, loss: 5.197714

step: 1470000, loss: 6.916918

step: 1480000, loss: 6.441984

step: 1490000, loss: 5.443647

step: 1500000, loss: 5.178482

step: 1510000, loss: 6.060414

step: 1520000, loss: 6.373306

step: 1530000, loss: 5.098322

step: 1540000, loss: 6.674916

step: 1550000, loss: 6.712685

step: 1560000, loss: 5.280202

step: 1570000, loss: 6.454964

step: 1580000, loss: 4.896697

step: 1590000, loss: 6.239226

step: 1600000, loss: 5.709726

Evaluation...

"five" nearest neighbors: three, four, two, six, seven, eight, one, zero,

"of" nearest neighbors: the, and, including, in, with, within, its, following,

"going" nearest neighbors: others, people, who, they, that, far, were, have,

"hardware" nearest neighbors: computer, systems, include, high, research, some, information, large,

"american" nearest neighbors: born, english, french, british, german, d, john, b,

"britain" nearest neighbors: country, military, china, europe, against, local, central, british,

step: 1610000, loss: 6.334940

step: 1620000, loss: 5.093616

step: 1630000, loss: 6.119366

step: 1640000, loss: 4.975187

step: 1650000, loss: 6.490408

step: 1660000, loss: 7.464082

step: 1670000, loss: 4.977184

step: 1680000, loss: 5.658133

step: 1690000, loss: 5.352454

step: 1700000, loss: 6.810776

step: 1710000, loss: 5.687447

step: 1720000, loss: 5.992206

step: 1730000, loss: 5.513011

step: 1740000, loss: 5.548522

step: 1750000, loss: 6.200248

step: 1760000, loss: 13.070073

step: 1770000, loss: 4.621058

step: 1780000, loss: 5.301342

step: 1790000, loss: 4.777030

step: 1800000, loss: 6.912136

Evaluation...

"five" nearest neighbors: three, four, six, seven, eight, two, nine, zero,

"of" nearest neighbors: the, in, first, from, became, and, following, under,

"going" nearest neighbors: others, their, through, which, therefore, open, how, that,

"hardware" nearest neighbors: computer, systems, include, research, standard, different, system, small,

"american" nearest neighbors: b, d, born, actor, UNK, english, nine, german,

"britain" nearest neighbors: china, country, europe, against, canada, military, island, including,

step: 1810000, loss: 5.584600

step: 1820000, loss: 5.619820

step: 1830000, loss: 6.078709

step: 1840000, loss: 5.052518

step: 1850000, loss: 5.430106

step: 1860000, loss: 7.396770

step: 1870000, loss: 5.344787

step: 1880000, loss: 5.937998

step: 1890000, loss: 5.706491

step: 1900000, loss: 5.140662

step: 1910000, loss: 5.607048

step: 1920000, loss: 5.407231

step: 1930000, loss: 6.238531

step: 1940000, loss: 5.567973

step: 1950000, loss: 4.894245

step: 1960000, loss: 6.104193

step: 1970000, loss: 5.282631

step: 1980000, loss: 6.189069

step: 1990000, loss: 6.169409

step: 2000000, loss: 6.470152

Evaluation...

"five" nearest neighbors: four, three, six, seven, eight, two, nine, zero,

"of" nearest neighbors: the, its, in, with, and, including, within, against,

"going" nearest neighbors: others, only, therefore, will, how, a, far, though,

"hardware" nearest neighbors: computer, systems, for, network, software, program, research, system,

"american" nearest neighbors: born, actor, d, italian, german, john, robert, b,

"britain" nearest neighbors: china, country, europe, canada, british, former, island, france,

step: 2010000, loss: 5.298714

step: 2020000, loss: 5.494207

step: 2030000, loss: 5.410875

step: 2040000, loss: 6.228232

step: 2050000, loss: 5.044596

step: 2060000, loss: 4.624638

step: 2070000, loss: 4.919327

step: 2080000, loss: 4.639625

step: 2090000, loss: 4.865627

step: 2100000, loss: 4.951073

step: 2110000, loss: 5.973768

step: 2120000, loss: 7.366824

step: 2130000, loss: 5.149571

step: 2140000, loss: 7.846234

step: 2150000, loss: 5.449315

step: 2160000, loss: 5.359211

step: 2170000, loss: 5.171029

step: 2180000, loss: 6.106437

step: 2190000, loss: 6.043995

step: 2200000, loss: 5.642351

Evaluation...

"five" nearest neighbors: four, three, six, two, eight, seven, zero, one,

"of" nearest neighbors: the, and, its, see, for, in, with, including,

"going" nearest neighbors: others, therefore, how, even, them, your, have, although,

"hardware" nearest neighbors: computer, systems, system, network, program, research, software, include,

"american" nearest neighbors: english, french, german, canadian, british, film, author, italian,

"britain" nearest neighbors: europe, china, country, germany, british, england, france, throughout,

step: 2210000, loss: 4.427110

step: 2220000, loss: 6.240989

step: 2230000, loss: 5.184978

step: 2240000, loss: 8.035570

step: 2250000, loss: 5.793781

step: 2260000, loss: 4.908427

step: 2270000, loss: 8.807668

step: 2280000, loss: 6.083229

step: 2290000, loss: 5.773360

step: 2300000, loss: 5.613671

step: 2310000, loss: 6.080076

step: 2320000, loss: 5.288568

step: 2330000, loss: 5.949232

step: 2340000, loss: 5.479994

step: 2350000, loss: 7.717686

step: 2360000, loss: 5.163609

step: 2370000, loss: 5.989407

step: 2380000, loss: 5.785729

step: 2390000, loss: 5.345478

step: 2400000, loss: 6.627133

Evaluation...

"five" nearest neighbors: three, four, six, two, seven, eight, zero, nine,

"of" nearest neighbors: the, in, and, including, from, within, its, with,

"going" nearest neighbors: therefore, people, they, out, only, according, your, now,

"hardware" nearest neighbors: computer, systems, network, program, system, software, run, design,

"american" nearest neighbors: author, born, actor, english, canadian, british, italian, d,

"britain" nearest neighbors: china, europe, country, throughout, france, canada, england, western,

step: 2410000, loss: 5.666146

step: 2420000, loss: 5.316198

step: 2430000, loss: 5.129625

step: 2440000, loss: 5.247949

step: 2450000, loss: 5.741394

step: 2460000, loss: 5.833083

step: 2470000, loss: 7.704844

step: 2480000, loss: 5.398345

step: 2490000, loss: 5.089633

step: 2500000, loss: 5.620508

step: 2510000, loss: 4.976034

step: 2520000, loss: 5.884676

step: 2530000, loss: 6.649922

step: 2540000, loss: 5.002588

step: 2550000, loss: 5.072144

step: 2560000, loss: 5.165375

step: 2570000, loss: 5.310089

step: 2580000, loss: 5.481957

step: 2590000, loss: 6.104440

step: 2600000, loss: 5.339644

Evaluation...

"five" nearest neighbors: three, four, six, seven, eight, nine, two, zero,

"of" nearest neighbors: the, first, from, with, became, in, following, and,

"going" nearest neighbors: how, therefore, back, will, through, always, your, make,

"hardware" nearest neighbors: computer, systems, system, network, program, technology, design, software,

"american" nearest neighbors: actor, singer, born, b, author, d, english, writer,

"britain" nearest neighbors: europe, china, throughout, great, england, france, country, india,

step: 2610000, loss: 7.754117

step: 2620000, loss: 5.979313

step: 2630000, loss: 5.394362

step: 2640000, loss: 4.866740

step: 2650000, loss: 5.219806

step: 2660000, loss: 6.074809

step: 2670000, loss: 6.216953

step: 2680000, loss: 5.944881

step: 2690000, loss: 5.863350

step: 2700000, loss: 6.128705

step: 2710000, loss: 5.502523

step: 2720000, loss: 5.300839

step: 2730000, loss: 6.358493

step: 2740000, loss: 6.058306

step: 2750000, loss: 4.689510

step: 2760000, loss: 6.032880

step: 2770000, loss: 5.844904

step: 2780000, loss: 5.385874

step: 2790000, loss: 5.370956

step: 2800000, loss: 4.912577

Evaluation...

"five" nearest neighbors: four, six, three, eight, seven, two, nine, one,

"of" nearest neighbors: in, the, and, from, including, following, with, under,

"going" nearest neighbors: your, then, through, will, how, so, back, even,

"hardware" nearest neighbors: computer, systems, program, network, design, standard, physical, software,

"american" nearest neighbors: actor, singer, born, author, writer, canadian, italian, d,

"britain" nearest neighbors: europe, china, england, throughout, france, india, great, germany,

step: 2810000, loss: 5.897756

step: 2820000, loss: 7.194932

step: 2830000, loss: 7.430175

step: 2840000, loss: 7.258231

step: 2850000, loss: 5.837617

step: 2860000, loss: 5.496673

step: 2870000, loss: 6.173716

step: 2880000, loss: 6.095749

step: 2890000, loss: 6.064944

step: 2900000, loss: 5.560488

step: 2910000, loss: 4.966107

step: 2920000, loss: 5.789579

1. step: 2930000, loss: 4.525987

step: 2940000, loss: 6.704808

step: 2950000, loss: 4.506433

step: 2960000, loss: 6.251270

step: 2970000, loss: 5.588204

step: 2980000, loss: 5.423235

step: 2990000, loss: 5.613834

step: 3000000, loss: 5.137326

Evaluation...

"five" nearest neighbors: four, three, six, seven, eight, two, zero, one,

"of" nearest neighbors: the, including, and, with, in, its, includes, within,

"going" nearest neighbors: how, they, when, them, make, always, your, though,

"hardware" nearest neighbors: computer, systems, network, program, physical, design, technology, software,

"american" nearest neighbors: canadian, english, australian, british, german, film, italian, author,

"britain" nearest neighbors: europe, england, china, throughout, india, france, great, british,

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  1. https://arxiv.org/pdf/1301.3781.pdf ↩︎

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