word2vect 代码讲解

导入需要的库

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

下载并读取语料库中的数据

首先运行如下代码，下载语料库。

# 第一步: 在下面这个地址下载语料库
url = 'http://mattmahoney.net/dc/'

def maybe_download(filename, expected_bytes):
    """
    这个函数的功能是：
        如果filename不存在，就在上面的地址下载它。
        如果filename存在，就跳过下载。
        最终会检查文字的字节数是否和expected_bytes相同。
    """
    if not os.path.exists(filename):
        print('start downloading...')
        filename, _ = urllib.request.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

# 下载语料库text8.zip并验证下载
filename = maybe_download('text8.zip', 31344016)

运行如下代码，将语料库转化为列表，并打印语料库单词长度以及前100个单词。

# 将语料库解压，并转换成一个word的list
def read_data(filename):
    """
    这个函数的功能是：
        将下载好的zip文件解压并读取为word的list
    """
    with zipfile.ZipFile(filename) as f:
        data = tf.compat.as_str(f.read(f.namelist()[0])).split()
    return data

vocabulary = read_data(filename)
print('Data size', len(vocabulary)) # 总长度为1700万左右
# 输出前100个词。
print(vocabulary[0:100])

打印输出结果如下图

语料库预处理，制作词表

# 第二步: 制作一个词表，将不常见的词变成一个UNK标识符
# 词表的大小为5万（即我们只考虑最常出现的5万个词）
vocabulary_size = 50000
def build_dataset(words, n_words):
    """
    函数功能：将原始的单词表示变成index
    """
    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  # UNK的index为0
            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

data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,
                                                            vocabulary_size)
del vocabulary  # 删除已节省内存
# 输出最常出现的5个单词
print('Most common words (+UNK)', count[:5])
# 输出转换后的数据库data，和原来的单词（前10个）
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

说明：

data : 转化为索引的数据集
count : 词频统计
dictionary : 单词到索引的映射
reverse_dictionary : 索引到单词的映射

上述代码打印结果如下图：

CBOW

定义模型生成 batch 的函数

# 第三步：定义一个函数，用于生成cbow模型用的batch
data_index = 0
def generate_batch(batch_size, cbow_window):
    global data_index
#    assert cbow_window % 2 == 1
    span = 2 * cbow_window + 1
    # 去除中心word: span - 1
    batch = np.ndarray(shape=(batch_size, span - 1), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)

    buffer = collections.deque(maxlen=span)
    for _ in range(span):
        buffer.append(data[data_index])
        # 循环选取 data中数据，到尾部则从头开始
        data_index = (data_index + 1) % len(data)
    
    for i in range(batch_size):
        # target at the center of span
        target = cbow_window
        # 仅仅需要知道context(word)而不需要word
        target_to_avoid = [cbow_window]

        col_idx = 0
        for j in range(span):
            # 略过中心元素 word
            if j == span // 2:
                continue
            batch[i, col_idx] = buffer[j]
            col_idx += 1
        labels[i, 0] = buffer[target]
        # 更新 buffer
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    data_index = (data_index + len(data) - span) % len(data)
    
    return batch, labels

训练

num_steps = 100001

if __name__ == '__main__':
    batch_size = 128
    embedding_size = 128 # Dimension of the embedding vector.
    cbow_window = 1 # How many words to consider left and right.
    num_skips = 2 # How many times to reuse an input to generate a label.
    # 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.
    # pick 16 samples from 100
    valid_examples = np.array(random.sample(range(valid_window), valid_size // 2))
    valid_examples = np.append(valid_examples, random.sample(range(1000, 1000+valid_window), valid_size // 2))
    num_sampled = 64 # Number of negative examples to sample.

    graph = tf.Graph()

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

        # Input data.
        train_dataset = tf.placeholder(tf.int32, shape=[batch_size,2 * cbow_window])
        train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
        valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

        # Variables.
        # embedding, vector for each word in the vocabulary
        embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        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]))

        # Model.
        # Look up embeddings for inputs.
        # this might efficiently find the embeddings for given ids (traind dataset)
        # manually doing this might not be efficient given there are 50000 entries in embeddings
        embeds = None
        for i in range(2 * cbow_window):
            embedding_i = tf.nn.embedding_lookup(embeddings, train_dataset[:,i])
            print('embedding %d shape: %s'%(i, embedding_i.get_shape().as_list()))
            emb_x,emb_y = embedding_i.get_shape().as_list()
            if embeds is None:
                embeds = tf.reshape(embedding_i, [emb_x,emb_y,1])
            else:
                embeds = tf.concat([embeds, tf.reshape(embedding_i, [emb_x, emb_y,1])], 2)

        assert embeds.get_shape().as_list()[2] == 2 * cbow_window
        print("Concat embedding size: %s"%embeds.get_shape().as_list())
        avg_embed =  tf.reduce_mean(embeds, 2, keep_dims=False)
        print("Avg embedding size: %s"%avg_embed.get_shape().as_list())


        loss = tf.reduce_mean(tf.nn.nce_loss(nce_weights, nce_biases,
                               labels=train_labels,
                               inputs=avg_embed,
                               num_sampled=num_sampled,
                               num_classes=vocabulary_size))

        # Optimizer.
        # 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.
        # Adagrad is required because there are too many things to optimize
        optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)

        # Compute the similarity between minibatch examples and all embeddings.
        # We use the cosine distance:
        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, tf.transpose(normalized_embeddings))

    with tf.Session(graph=graph) as session:
        tf.global_variables_initializer().run()
        print('Initialized')
        average_loss = 0
        for step in range(num_steps):
            batch_data, batch_labels = generate_batch(batch_size, cbow_window)
            feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
            _, l = session.run([optimizer, loss], feed_dict=feed_dict)
            average_loss += l
            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]
                    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)
        final_embeddings = normalized_embeddings.eval()

可视化

# Step 6: 可视化
# 可视化的图片会保存为“tsne1.png”

def plot_with_labels(low_dim_embs, labels, filename='tsne1.png'):
    assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
    plt.figure(figsize=(18, 18))  # 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:
    # pylint: disable=g-import-not-at-top
    from sklearn.manifold import TSNE
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    # 因为我们的embedding的大小为128维，没有办法直接可视化
    # 所以我们用t-SNE方法进行降维
    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
    # 只画出500个词的位置
    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 show embeddings.')

运行代码后，生成结果如下图：

完整代码

# coding: utf-8

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic word2vec example."""

# 导入一些需要的库
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


# 第一步: 在下面这个地址下载语料库
url = 'http://mattmahoney.net/dc/'


def maybe_download(filename, expected_bytes):
    """
    这个函数的功能是：
        如果filename不存在，就在上面的地址下载它。
        如果filename存在，就跳过下载。
        最终会检查文字的字节数是否和expected_bytes相同。
    """
    if not os.path.exists(filename):
        print('start downloading...')
        filename, _ = urllib.request.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

# 下载语料库text8.zip并验证下载
filename = maybe_download('text8.zip', 31344016)

# 将语料库解压，并转换成一个word的list
def read_data(filename):
    """
    这个函数的功能是：
        将下载好的zip文件解压并读取为word的list
    """
    with zipfile.ZipFile(filename) as f:
        data = tf.compat.as_str(f.read(f.namelist()[0])).split()
    return data

vocabulary = read_data(filename)
print('Data size', len(vocabulary)) # 总长度为1700万左右
# 输出前100个词。
print(vocabulary[0:100])



# 第二步: 制作一个词表，将不常见的词变成一个UNK标识符
# 词表的大小为5万（即我们只考虑最常出现的5万个词）
vocabulary_size = 50000


def build_dataset(words, n_words):
    """
    函数功能：将原始的单词表示变成index
    """
    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  # UNK的index为0
            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

data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,
                                                            vocabulary_size)
del vocabulary  # 删除已节省内存
# 输出最常出现的5个单词
print('Most common words (+UNK)', count[:5])
# 输出转换后的数据库data，和原来的单词（前10个）
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
# 我们下面就使用data来制作训练集
print ("+++++++++++++++++")
data_index = 0

# 第三步：定义一个函数，用于生成cbow模型用的batch
def generate_batch(batch_size, cbow_window):
    global data_index
    assert cbow_window % 2 == 1
    span = 2 * cbow_window + 1
    # 去除中心word: span - 1
    batch = np.ndarray(shape=(batch_size, span - 1), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)

    buffer = collections.deque(maxlen=span)
    for _ in range(span):
        buffer.append(data[data_index])
        # 循环选取 data中数据，到尾部则从头开始
        data_index = (data_index + 1) % len(data)
    
    for i in range(batch_size):
        # target at the center of span
        target = cbow_window
        # 仅仅需要知道context(word)而不需要word
        target_to_avoid = [cbow_window]

        col_idx = 0
        for j in range(span):
            # 略过中心元素 word
            if j == span // 2:
                continue
            batch[i, col_idx] = buffer[j]
            col_idx += 1
        labels[i, 0] = buffer[target]
        # 更新 buffer
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)

    return batch, labels

num_steps = 100001

if __name__ == '__main__':
    batch_size = 128
    embedding_size = 128 # Dimension of the embedding vector.
    cbow_window = 1 # How many words to consider left and right.
    num_skips = 2 # How many times to reuse an input to generate a label.
    # 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.
    # pick 16 samples from 100
    valid_examples = np.array(random.sample(range(valid_window), valid_size // 2))
    valid_examples = np.append(valid_examples, random.sample(range(1000, 1000+valid_window), valid_size // 2))
    num_sampled = 64 # Number of negative examples to sample.

    graph = tf.Graph()

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

        # Input data.
        train_dataset = tf.placeholder(tf.int32, shape=[batch_size,2 * cbow_window])
        train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
        valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

        # Variables.
        # embedding, vector for each word in the vocabulary
        embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        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]))

        # Model.
        # Look up embeddings for inputs.
        # this might efficiently find the embeddings for given ids (traind dataset)
        # manually doing this might not be efficient given there are 50000 entries in embeddings
        embeds = None
        for i in range(2 * cbow_window):
            embedding_i = tf.nn.embedding_lookup(embeddings, train_dataset[:,i])
            print('embedding %d shape: %s'%(i, embedding_i.get_shape().as_list()))
            emb_x, emb_y = embedding_i.get_shape().as_list()
            if embeds is None:
                embeds = tf.reshape(embedding_i, [emb_x,emb_y,1])
            else:
                embeds = tf.concat([embeds, tf.reshape(embedding_i, [emb_x, emb_y,1])], 2)

        assert embeds.get_shape().as_list()[2] == 2 * cbow_window
        print("Concat embedding size: %s"%embeds.get_shape().as_list())
        avg_embed =  tf.reduce_mean(embeds, 2, keep_dims=False)
        print("Avg embedding size: %s"%avg_embed.get_shape().as_list())

        loss = tf.reduce_mean(tf.nn.nce_loss(nce_weights, nce_biases,
                               labels=train_labels,
                               inputs=avg_embed,
                               num_sampled=num_sampled,
                               num_classes=vocabulary_size))

        # Optimizer.
        # 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.
        # Adagrad is required because there are too many things to optimize
        optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)

        # Compute the similarity between minibatch examples and all embeddings.
        # We use the cosine distance:
        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, tf.transpose(normalized_embeddings))

    with tf.Session(graph=graph) as session:
        tf.global_variables_initializer().run()
        print('Initialized')
        average_loss = 0
        for step in range(num_steps):
            batch_data, batch_labels = generate_batch(batch_size, cbow_window)
            feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
            _, l = session.run([optimizer, loss], feed_dict=feed_dict)
            average_loss += l
            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]
                    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)
        final_embeddings = normalized_embeddings.eval()

# Step 6: 可视化
# 可视化的图片会保存为“tsne1.png”

def plot_with_labels(low_dim_embs, labels, filename='tsne1.png'):
    assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
    plt.figure(figsize=(18, 18))  # 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:
    # pylint: disable=g-import-not-at-top
    from sklearn.manifold import TSNE
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    # 因为我们的embedding的大小为128维，没有办法直接可视化
    # 所以我们用t-SNE方法进行降维
    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
    # 只画出500个词的位置
    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 show embeddings.')

Skip-gram

制作训练集

# 我们下面就使用data来制作训练集
data_index = 0

# 第三步：定义一个函数，用于生成skip-gram模型用的batch
def generate_batch(batch_size, num_skips, skip_window):
    # data_index相当于一个指针，初始为0
    # 每次生成一个batch，data_index就会相应地往后推
    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 ]
    buffer = collections.deque(maxlen=span)
    # data_index是当前数据开始的位置
    # 产生batch后就往后推1位（产生batch）
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    for i in range(batch_size // num_skips):#//运算符：取整除 - 返回商的整数部分（向下取整）
        # 利用buffer生成batch
        # buffer是一个长度为 2 * skip_window + 1长度的word list
        # 一个buffer生成num_skips个数的样本
        # print([reverse_dictionary[i] for i in buffer])
        target = skip_window  # target label at the center of the buffer
        # targets_to_avoid保证样本不重复
        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])
        # 每利用buffer生成num_skips个样本，data_index就向后推进一位
        data_index = (data_index + 1) % len(data)
    data_index = (data_index + len(data) - span) % len(data)
    return batch, labels

我们运行如下代码，试着打印一下生成的训练集的 batch

# 默认情况下skip_window=1, num_skips=2
# 此时就是从连续的3(3 = skip_window*2 + 1)个词中生成2(num_skips)个样本。
# 如连续的三个词['used', 'against', 'early']
# 生成两个样本：against -> used, against -> early
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]])

运行结果如下图：

建立模型

# 第四步: 建立模型.

batch_size = 128
embedding_size = 128  # 词嵌入空间是128维的。即word2vec中的vec是一个128维的向量
skip_window = 1       # skip_window参数和之前保持一致
num_skips = 2         # num_skips参数和之前保持一致

# 在训练过程中，会对模型进行验证 
# 验证的方法就是找出和某个词最近的词。
# 只对前valid_window的词进行验证，因为这些词最常出现
valid_size = 16     # 每次验证16个词
valid_window = 100  # 这16个词是在前100个最常见的词中选出来的
valid_examples = np.random.choice(valid_window, valid_size, replace=False)

# 构造损失时选取的噪声词的数量
num_sampled = 64

graph = tf.Graph()

with graph.as_default():
    # 输入的batch
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    # 用于验证的词
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
    
    # 下面采用的某些函数还没有gpu实现，所以我们只在cpu上定义模型
    with tf.device('/cpu:0'):
        # 定义1个embeddings变量，相当于一行存储一个词的embedding
        embeddings = tf.Variable(
            tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        # 利用embedding_lookup可以轻松得到一个batch内的所有的词嵌入
        embed = tf.nn.embedding_lookup(embeddings, train_inputs)

        # 创建两个变量用于NCE Loss（即选取噪声词的二分类损失）
        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]))

    # tf.nn.nce_loss会自动选取噪声词，并且形成损失。
    # 随机选取num_sampled个噪声词
    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))

    # 得到loss后，我们就可以构造优化器了
    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

    # 计算词和词的相似度（用于验证）
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    # 找出和验证词的embedding并计算它们和所有单词的相似度
    valid_embeddings = tf.nn.embedding_lookup(
        normalized_embeddings, valid_dataset)
    similarity = tf.matmul(
        valid_embeddings, normalized_embeddings, transpose_b=True)

    # 变量初始化步骤
    init = tf.global_variables_initializer()

训练

# 第五步：开始训练
num_steps = 100001

with tf.Session(graph=graph) as session:
    # 初始化变量
    init.run()
    print('Initialized')

    average_loss = 0
    for step in xrange(num_steps):
        batch_inputs, batch_labels = generate_batch(
            batch_size, num_skips, skip_window)
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

        # 优化一步
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val

        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
            # 2000个batch的平均损失
            print('Average loss at step ', step, ': ', average_loss)
            average_loss = 0

        # 每1万步，我们进行一次验证
        if step % 10000 == 0:
            # sim是验证词与所有词之间的相似度
            sim = similarity.eval()
            # 一共有valid_size个验证词
            for i in xrange(valid_size):
                valid_word = reverse_dictionary[valid_examples[i]]
                top_k = 8  # 输出最相邻的8个词语
                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)
    # final_embeddings是我们最后得到的embedding向量
    # 它的形状是[vocabulary_size, embedding_size]
    # 每一行就代表着对应index词的词嵌入表示
    final_embeddings = normalized_embeddings.eval()

我们可以看到最近一次的训练结果，词语的相似度越大，语义约接近

可视化

# Step 6: 可视化
# 可视化的图片会保存为“tsne.png”

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))  # 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:
    # pylint: disable=g-import-not-at-top
    from sklearn.manifold import TSNE
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    # 因为我们的embedding的大小为128维，没有办法直接可视化
    # 所以我们用t-SNE方法进行降维
    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
    # 只画出500个词的位置
    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 show embeddings.')

可视化结果如下图

完整代码

# coding: utf-8

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic word2vec example."""

# 导入一些需要的库
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


# 第一步: 在下面这个地址下载语料库
url = 'http://mattmahoney.net/dc/'

def maybe_download(filename, expected_bytes):
    """
    这个函数的功能是：
        如果filename不存在，就在上面的地址下载它。
        如果filename存在，就跳过下载。
        最终会检查文字的字节数是否和expected_bytes相同。
    """
    if not os.path.exists(filename):
        print('start downloading...')
        filename, _ = urllib.request.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

# 下载语料库text8.zip并验证下载
filename = maybe_download('text8.zip', 31344016)

# 将语料库解压，并转换成一个word的list
def read_data(filename):
    """
    这个函数的功能是：
        将下载好的zip文件解压并读取为word的list
    """
    with zipfile.ZipFile(filename) as f:
        data = tf.compat.as_str(f.read(f.namelist()[0])).split()
    return data

vocabulary = read_data(filename)
print('Data size', len(vocabulary)) # 总长度为1700万左右
# 输出前100个词。
print(vocabulary[0:100])


# 第二步: 制作一个词表，将不常见的词变成一个UNK标识符
# 词表的大小为5万（即我们只考虑最常出现的5万个词）
vocabulary_size = 50000
def build_dataset(words, n_words):
    """
    函数功能：将原始的单词表示变成index
    """
    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  # UNK的index为0
            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

data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,
                                                            vocabulary_size)
del vocabulary  # 删除已节省内存
# 输出最常出现的5个单词
print('Most common words (+UNK)', count[:5])
# 输出转换后的数据库data，和原来的单词（前10个）
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
# 我们下面就使用data来制作训练集
data_index = 0

# 第三步：定义一个函数，用于生成skip-gram模型用的batch
def generate_batch(batch_size, num_skips, skip_window):
    # data_index相当于一个指针，初始为0
    # 每次生成一个batch，data_index就会相应地往后推
    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 ]
    buffer = collections.deque(maxlen=span)
    # data_index是当前数据开始的位置
    # 产生batch后就往后推1位（产生batch）
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    for i in range(batch_size // num_skips):#//运算符：取整除 - 返回商的整数部分（向下取整）
        # 利用buffer生成batch
        # buffer是一个长度为 2 * skip_window + 1长度的word list
        # 一个buffer生成num_skips个数的样本
        # print([reverse_dictionary[i] for i in buffer])
        target = skip_window  # target label at the center of the buffer
        # targets_to_avoid保证样本不重复
        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])
        # 每利用buffer生成num_skips个样本，data_index就向后推进一位
        data_index = (data_index + 1) % len(data)
    data_index = (data_index + len(data) - span) % len(data)
    return batch, labels

# 默认情况下skip_window=1, num_skips=2
# 此时就是从连续的3(3 = skip_window*2 + 1)个词中生成2(num_skips)个样本。
# 如连续的三个词['used', 'against', 'early']
# 生成两个样本：against -> used, against -> early
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]])


# 第四步: 建立模型.

batch_size = 128
embedding_size = 128  # 词嵌入空间是128维的。即word2vec中的vec是一个128维的向量
skip_window = 1       # skip_window参数和之前保持一致
num_skips = 2         # num_skips参数和之前保持一致

# 在训练过程中，会对模型进行验证 
# 验证的方法就是找出和某个词最近的词。
# 只对前valid_window的词进行验证，因为这些词最常出现
valid_size = 16     # 每次验证16个词
valid_window = 100  # 这16个词是在前100个最常见的词中选出来的
valid_examples = np.random.choice(valid_window, valid_size, replace=False)

# 构造损失时选取的噪声词的数量
num_sampled = 64

graph = tf.Graph()

with graph.as_default():
    # 输入的batch
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    # 用于验证的词
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # 下面采用的某些函数还没有gpu实现，所以我们只在cpu上定义模型
    with tf.device('/cpu:0'):
        # 定义1个embeddings变量，相当于一行存储一个词的embedding
        embeddings = tf.Variable(
            tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        # 利用embedding_lookup可以轻松得到一个batch内的所有的词嵌入
        embed = tf.nn.embedding_lookup(embeddings, train_inputs)

        # 创建两个变量用于NCE Loss（即选取噪声词的二分类损失）
        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]))

    # tf.nn.nce_loss会自动选取噪声词，并且形成损失。
    # 随机选取num_sampled个噪声词
    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))

    # 得到loss后，我们就可以构造优化器了
    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

    # 计算词和词的相似度（用于验证）
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    # 找出和验证词的embedding并计算它们和所有单词的相似度
    valid_embeddings = tf.nn.embedding_lookup(
        normalized_embeddings, valid_dataset)
    similarity = tf.matmul(
        valid_embeddings, normalized_embeddings, transpose_b=True)

    # 变量初始化步骤
    init = tf.global_variables_initializer()


# 第五步：开始训练
num_steps = 100001

with tf.Session(graph=graph) as session:
    # 初始化变量
    init.run()
    print('Initialized')

    average_loss = 0
    for step in xrange(num_steps):
        batch_inputs, batch_labels = generate_batch(
            batch_size, num_skips, skip_window)
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

        # 优化一步
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val

        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
            # 2000个batch的平均损失
            print('Average loss at step ', step, ': ', average_loss)
            average_loss = 0

        # 每1万步，我们进行一次验证
        if step % 10000 == 0:
            # sim是验证词与所有词之间的相似度
            sim = similarity.eval()
            # 一共有valid_size个验证词
            for i in xrange(valid_size):
                valid_word = reverse_dictionary[valid_examples[i]]
                top_k = 8  # 输出最相邻的8个词语
                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)
    # final_embeddings是我们最后得到的embedding向量
    # 它的形状是[vocabulary_size, embedding_size]
    # 每一行就代表着对应index词的词嵌入表示
    final_embeddings = normalized_embeddings.eval()

# Step 6: 可视化
# 可视化的图片会保存为“tsne.png”

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))  # 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:
    # pylint: disable=g-import-not-at-top
    from sklearn.manifold import TSNE
    import matplotlib
    matplotlib.use('agg')
    import matplotlib.pyplot as plt
    # 因为我们的embedding的大小为128维，没有办法直接可视化
    # 所以我们用t-SNE方法进行降维
    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
    # 只画出500个词的位置
    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 show embeddings.')