tensorflow在文本处理中的使用——CBOW词嵌入模型

代码来源于：tensorflow机器学习实战指南（曾益强 译，2017年9月）——第七章：自然语言处理

代码地址：https://github.com/nfmcclure/tensorflow-cookbook

数据：http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz

CBOW概念图：

 

步骤如下：

• 必要包
• 声明模型参数
• 读取数据集
• 创建单词字典，转换句子列表为单词索引列表
• 生成批量数据
• 构建图
• 训练

---

step1：必要包

参考：tensorflow在文本处理中的使用——skip-gram模型

---

step2：声明模型参数

# Declare model parameters
batch_size = 500
embedding_size = 200
vocabulary_size = 2000
generations = 50000
model_learning_rate = 0.001

num_sampled = int(batch_size/2)    # Number of negative examples to sample.
window_size = 3       # How many words to consider left and right.

# Add checkpoints to training
save_embeddings_every = 5000
print_valid_every = 5000
print_loss_every = 100

# Declare stop words
stops = stopwords.words('english')

# We pick some test words. We are expecting synonyms to appear
valid_words = ['love', 'hate', 'happy', 'sad', 'man', 'woman']

---

step3：读取数据集

参考：tensorflow在文本处理中的使用——辅助函数

---

step4：创建单词字典，转换句子列表为单词索引列表

参考：tensorflow在文本处理中的使用——辅助函数

---

step5：生成批量数据

看一下单步执行的中间结果，利于更好理解处理过程:

>>> rand_sentence=[2520, 1421, 146, 1215, 5, 468, 12, 14, 18, 20]
>>> window_size = 3

#类似skip-gram
>>> window_sequences = [rand_sentence[max((ix-window_size),0):(ix+window_size+1)] for ix, x in enumerate(rand_sentence)]
>>> label_indices = [ix if ix<window_size else window_size for ix,x in enumerate(window_sequences)]
>>> window_sequences
[[2520, 1421, 146, 1215], [2520, 1421, 146, 1215, 5], [2520, 1421, 146, 1215, 5, 468], [2520, 1421, 146, 1215, 5, 468, 12], [1421, 146, 1215, 5, 468, 12, 14], [146, 1215, 5, 468, 12, 14, 18], [1215, 5, 468, 12, 14, 18, 20], [5, 468, 12, 14, 18, 20], [468, 12, 14, 18, 20], [12, 14, 18, 20]]
>>> label_indices
[0, 1, 2, 3, 3, 3, 3, 3, 3, 3]

#生成input和label
>>> batch_and_labels = [(x[:y] + x[(y+1):], x[y]) for x,y in zip(window_sequences, label_indices)]
>>> batch_and_labels = [(x,y) for x,y in batch_and_labels if len(x)==2*window_size]
>>> batch, labels = [list(x) for x in zip(*batch_and_labels)]
>>> batch_and_labels
[([2520, 1421, 146, 5, 468, 12], 1215), ([1421, 146, 1215, 468, 12, 14], 5), ([146, 1215, 5, 12, 14, 18], 468), ([1215, 5, 468, 14, 18, 20], 12)]
>>> batch
[[2520, 1421, 146, 5, 468, 12], [1421, 146, 1215, 468, 12, 14], [146, 1215, 5, 12, 14, 18], [1215, 5, 468, 14, 18, 20]]
>>> labels
[1215, 5, 468, 12]

---

step6：构建图

# Define Embeddings:
embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))

# NCE loss parameters
nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / np.sqrt(embedding_size)))
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

# Create data/target placeholders
x_inputs = tf.placeholder(tf.int32, shape=[batch_size, 2*window_size])
y_target = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

# Lookup the word embedding
# Add together window embeddings:CBOW模型将上下文窗口内的单词嵌套叠加在一起
embed = tf.zeros([batch_size, embedding_size])
for element in range(2*window_size):
    embed += tf.nn.embedding_lookup(embeddings, x_inputs[:, element])

# Get loss from prediction
loss = tf.reduce_mean(tf.nn.nce_loss(nce_weights, nce_biases, embed, y_target, num_sampled, vocabulary_size))
                                     
# Create optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=model_learning_rate).minimize(loss)

# Cosine similarity between words计算验证单词集
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, normalized_embeddings, transpose_b=True)

# Create model saving operation该方法默认会保存整个计算图会话，本例中指定参数只保存嵌套变量并设置名字
saver = tf.train.Saver({"embeddings": embeddings})

---

step7：训练 

#Add variable initializer.
init = tf.initialize_all_variables()
sess.run(init)

# Run the skip gram model.
print('Starting Training')
loss_vec = []
loss_x_vec = []
for i in range(generations):
    batch_inputs, batch_labels = text_helpers.generate_batch_data(text_data, batch_size, window_size, method='cbow')
    feed_dict = {x_inputs : batch_inputs, y_target : batch_labels}

    # Run the train step
    sess.run(optimizer, feed_dict=feed_dict)

    # Return the loss
    if (i+1) % print_loss_every == 0:
        loss_val = sess.run(loss, feed_dict=feed_dict)
        loss_vec.append(loss_val)
        loss_x_vec.append(i+1)
        print('Loss at step {} : {}'.format(i+1, loss_val))
      
    # Validation: Print some random words and top 5 related words
    if (i+1) % print_valid_every == 0:
        sim = sess.run(similarity, feed_dict=feed_dict)
        for j in range(len(valid_words)):
            valid_word = word_dictionary_rev[valid_examples[j]]
            top_k = 5 # number of nearest neighbors
            nearest = (-sim[j, :]).argsort()[1:top_k+1]
            log_str = "Nearest to {}:".format(valid_word)
            for k in range(top_k):
                close_word = word_dictionary_rev[nearest[k]]
                log_str = '{} {},' .format(log_str, close_word)
            print(log_str)
            
    # Save dictionary + embeddings
    if (i+1) % save_embeddings_every == 0:
        # Save vocabulary dictionary
        with open(os.path.join(data_folder_name,'movie_vocab.pkl'), 'wb') as f:
            pickle.dump(word_dictionary, f)
        
        # Save embeddings
        model_checkpoint_path = os.path.join(os.getcwd(),data_folder_name,'cbow_movie_embeddings.ckpt')
        save_path = saver.save(sess, model_checkpoint_path)
        print('Model saved in file: {}'.format(save_path))

 运行结果：

工作原理：Word2Vec嵌套的CBOW模型和skip-gram模型非常相似。主要不同点是生成数据和单词嵌套的处理。加载文本数据，归一化文本，创建词汇字典，使用词汇字典查找嵌套，组合嵌套并训练神经网络模型预测目标单词。

延伸学习：CBOW方法是在上下文窗口内单词嵌套叠加上进行训练并预测目标单词的。Word2Vec的CBOW方法更平滑，更适用于小文本数据集。