TensorFlow官方文档中文版-笔记(七)

实现word2vec中skip_gram模型

建立图形

先定义一个嵌套参数矩阵。我们用唯一的随机值来初始化这个大矩阵。

embeddings = tf.Variable(
    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))

对噪声-比对的损失计算就使用一个逻辑回归模型。对此，我们需要对语料库中的每个单词定义一个权重值和偏差值。(也可称之为输出权重 与之对应的 输入嵌套值)。定义如下。

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]))

Skip-Gram模型有两个输入。一个是一组用整型表示的上下文单词，另一个是目标单词。给这些输入建立占位符节点，之后就可以填入数据了。

# 建立输入占位符
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])

然后我们需要对批数据中的单词建立嵌套向量，TensorFlow提供了方便的工具函数。

embed = tf.nn.embedding_lookup(embeddings, train_inputs)

好了，现在我们有了每个单词的嵌套向量，接下来就是使用噪声-比对的训练方式来预测目标单词。

# 计算 NCE 损失函数, 每次使用负标签的样本.
loss = tf.reduce_mean(
  tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,num_sampled, vocabulary_size))

我们对损失函数建立了图形节点，然后我们需要计算相应梯度和更新参数的节点，比如说在这里我们会使用随机梯度下降法，TensorFlow也已经封装好了该过程。

# 使用 SGD 控制器.
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0).minimize(loss)

训练模型

训练的过程很简单，只要在循环中使用feed_dict不断给占位符填充数据，同时调用 session.run即可。

 for step in range(num_steps):
        # 先使用generate_batch生成一个batch的inputs和labels数据
        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
        # 并用他们创建feed_dict
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}
        # 然后执行一次优化器运算（即一次参数更新）和损失计算
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)

完整代码如下：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
from tempfile import gettempdir
import zipfile

import numpy as np
import urllib
# from six.moves import urllib
# from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

# 解压下载的压缩文件，并使用tf.compat.as_str将数据转成单词的列表。
def read_data(filename):
    with zipfile.ZipFile(filename) as f:
        data = tf.compat.as_str(f.read(f.namelist()[0])).split()
    return data


# vocabulary是将文本分词后的所有单词，按照文本顺序保存
vocabulary = read_data("/Users/iris/Documents/数据集/text8.zip")
print('Data size', len(vocabulary))

vocabulary_size = 50000


# 创建vocabulary词汇表
def build_dataset(words, n_words):
    # 使用collections.Counter统计单词列表中单词的频数，然后使用most_common方法提取top 50000频数的单词作为vocabulary
    count = [['UNK', -1]]
    count.extend(collections.Counter(words).most_common(n_words - 1))  # count[['UNK', -1],[word,word频数]，。。。]

    # 将top 50000 词汇的vocabulary转为编号，以频数排序
    dictionary = dict()
    for word, _ in count:
        dictionary[word] = len(dictionary)  # dictionary [word: 编号]

    # 遍历单词列表，判断每个单词是否出现在dictionary中，是则转换为dictionary中按频数排序的编号，不是则转为编号0
    data = list()
    unk_count = 0
    for word in words:
        index = dictionary.get(word, 0)  # 查找word，若不存在返回默认值0
        if index == 0:  # dictionary['UNK']
            unk_count += 1
        data.append(index)  # data (w1编号，w2编号，。。。，wn编号)

    # 更新count的第一个数据
    count[0][1] = unk_count

    reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    # 最后返回转换后的编码(data)、每个单词的频数统计(count)、词汇表(dictionary)及其反转的形式(reverse_dictionary)
    return data, count, dictionary, reversed_dictionary

data, count, dictionary, reverse_dictionary = build_dataset(vocabulary, vocabulary_size)

# 删除原始单词列表，可以节约内存，再打印出最高频出现的词汇及其数量
del vocabulary  # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0  # 定义data_index为全局变量，因为我们会反复调用generate_batch


# 生成训练用的batch数据：
#  batch_size为batch的大小
#  num_skips为对每个单词生成多少个样本(不能大于skip_window值的两倍，batch_size必须是它的整数倍(确保每个batch包含了一个词汇对应的所有样本))
#  skip_window指单词最远可以联系的距离
def generate_batch(batch_size, num_skips, skip_window):
    global data_index
    # 断言是声明其布尔值必须为真的判定，如果发生异常就说明表达示为假
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window
    # np.ndarray 初始化为数组，shape 生成数组形状，dtype 数据类型
    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
    # span为对某个单词创建相关样本时会使用到的单词数量，包括目标单词本身和它前后的单词
    span = 2 * skip_window + 1
    # deque是double-ended queue的缩写，双端队列结构。最大容量为span，在对deque执行append方法添加变量时，只会保留最后插入的span个变量
    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
    # 每次循环对一个目标单词生成样本
    for i in range(batch_size // num_skips):  # //整数除法
        context_words = [w for w in range(span) if w != skip_window]  # 输出0~span中所有数值，除了skip_window
        # print(context_words)
        words_to_use = random.sample(context_words, num_skips)
        # print(words_to_use)
        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[:] = data[:span]
            data_index = span
        else:
            buffer.append(data[data_index])
            data_index += 1
    data_index = (data_index + len(data) - span) % len(data)
    return batch, labels


# 这里调用generate_batch函数测试一下其功能
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]])

##########################################################################################################
# Step1、2 定义输入和定义算法公式

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.
num_sampled = 64  # Number of negative examples to sample.

valid_size = 16  # Random set of words to evaluate similarity on.用来抽取的验证单词数
valid_window = 100  # Only pick dev samples in the head of the distribution.指验证单词只从频数最高的100个单词中抽取
valid_examples = np.random.choice(valid_window, valid_size, replace=False)  # 使用np.random.choice函数进行随机抽取

# 下面开始定义Skip-Gram WordVec模型的网络结构

graph = tf.Graph()

# 返回一个上下文管理器,使得这个Graph对象成为当前默认的graph.
# with关键字让这个代码块内创建的从操作(ops)添加到这个新的图里面.
# 让with下面代码块的操作都执行在这个图中，作为默认graph
with graph.as_default():
    #  Input data. 创建训练数据中的inputs和labels的placeholder，placeholder给节点输入数据
    #  placeholder是占位符，相当于java中的定义
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    # 将前面产生的valid_examples转为TensorFlow中的constant
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # 限定所有计算再CPU上执行
    # 以下代码块操作是初始化所有参数
    with tf.device('/cpu:0'):
        # 随机生成所有单词的词向量embeddings
        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。
        # 在embeddings中查找train_inputs对应的表示，赋值给embed
        embed = tf.nn.embedding_lookup(embeddings, train_inputs)

        # NCE Loss作为训练的优化目标
        # 使用truncated_normal初始化NCE Loss中的权重参数nce_weights
        nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size)))
        # 将nce_biases初始化为0
        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))


# Step3 定义损失函数
    # 使用tf.nn.nce_loss计算学习出的词向量embedding在训练数据上的loss，并使用tf.reduce_mean进行汇总
    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))

# Step4 定义优化算法
    # 定义优化器为SGD并且学习速率为1.0
    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

    # Compute the cosine similarity between minibatch examples and all embeddings.
    # 计算embeddings的L2范数norm
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    # embeddings除以L2范数到标准化后的normalized_embeddings
    normalized_embeddings = embeddings / norm
    # 查询验证单词的嵌入向量embeddings
    valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)
    # 计算并验证单词的嵌入向量与词汇表中所有单词的相似性
    similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)

    # Add variable initializer.
    init = tf.global_variables_initializer()  # 最后初始化所有模型参数


# Step5 迭代执行训练操作
num_steps = 100001

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 range(num_steps):
        # 先使用generate_batch生成一个batch的inputs和labels数据
        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
        # 并用他们创建feed_dict
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

        # 然后执行一次优化器运算（即一次参数更新）和损失计算
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val  # 并将这一步训练的loss累积到average_loss

        # 之后每2000次循环，计算一下平均loss并显示出来
        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

        # 每10000次循环，计算一次验证单词与全部单词的相似度，并将与每个验证单词最相似的8个单词展示出来
        # 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_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)
    final_embeddings = normalized_embeddings.eval()


# Step 6: Visualize the embeddings.


# pylint: disable=missing-docstring
# Function to draw visualization of distance between embeddings.
# 下面定义一个用来可视化Word2Vec效果的函数，low_dim_embs是降维到2维的单词的空间向量，将在图表中展示每个单词的位置
def plot_with_labels(low_dim_embs, labels, filename):
    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, :]
        # 显示散点图
        plt.scatter(x, y)
        # 展示单词本身
        plt.annotate(label,
                     xy=(x, y),
                     xytext=(5, 2),
                     textcoords='offset points',
                     ha='right',
                     va='bottom')
    # 保存图片到本地文件
    plt.savefig(filename)


# 实现降维，将原始的128维降到2维，再使用plot_with_labels进行展示，这里只展示词频最高的100个单词的可视化结果
try:
    # pylint: disable=g-import-not-at-top
    from sklearn.manifold import TSNE
    import matplotlib.pyplot as plt

    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
    plot_only = 500
    low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
    labels = [reverse_dictionary[i] for i in range(plot_only)]
    plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png'))

except ImportError as ex:
    print('Please install sklearn, matplotlib, and scipy to show embeddings.')
    print(ex)