算法原理：

超参数

# Training Parameters
learning_rate = 0.1  
batch_size = 128	
num_steps = 3000000
display_step = 10000
eval_step = 200000
# Evaluation Parameters
valid_size = 20  
valid_window = 100 
#从词典的前100个词中随机选取20个词来验证模型
eval_words = np.random.choice(valid_window, valid_size, replace=False)
# Word2Vec Parameters
embedding_size = 200  
max_vocabulary_size = 50000  
min_occurrence = 10  # 词典中词出现的最低次数
skip_window = 3  # 窗口大小
num_skips = 2  # 每个输入中心词在其上下文区间中选取num_skips个词来生成样本
num_sampled = 64  # Number of negative examples

解释：

我们模型的验证是：计算于eval_words数组中的词最近似的几个词

词典生成模块

def make_vocabulary(data):
	"""
	data:是一个一维的list，每个元素可以是单个字也可以是切词后的词
	data是我们将句子切词后再拼接生成的（如果以字为单位不用切词直接拼接）
	"""
    word2count = [('UNK', -1)]
    #统计语言库词的次数
    word2count.extend(collections.Counter("".join(data)).most_common(max_vocabulary_size - 1))
	#去掉出现次数比较少的词
    for i in range(len(word2count) - 1, -1, -1):
        if word2count[i][1] < min_occurrence:
            word2count.pop(i)
        else:
            break

    vocabulary_size = len(word2count)
	
    word2id = dict()
    for i, (word, _) in enumerate(word2count):
        word2id[word] = i
	#将data中的词转化为其对应索引ID
    data_id = list()
    unk_count = 0
    for word in data:
        index = word2id.get(word, 0)
        if index == 0:
            unk_count += 1
        data_id.append(index)
    word2count[0] = ('UNK', unk_count)#UNK用来代替词典里面有的词
    id2word = dict(zip(word2id.values(), word2id.keys()))
    return data_id,word2id,id2word,word2count,vocabulary_size

batch生成模块

data_index = 0  #上面模块生成的data_id的跟踪索引
# Generate training batch for the skip-gram model
def next_batch(batch_size, num_skips, skip_window,data):
	#data就是上一个模块生成的data_id
    global data_index
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window
    #num_skips是每个中心词生成的样本数所以要求batch_size是它的倍数
    # 确定了中心词后其上下文词个数为左skip_window个右 skip_window个
    #对每个中心生成的样本的label是从这2 * skip_windo上下文中随机抽取的。
    #所以样本数num_skips 要小于窗口大小
    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
    # get window size (words left and right + current one)
    span = 2 * skip_window + 1  # 7
    buffer = collections.deque(maxlen=span)  # 一个双向队列
    if data_index + span > len(data):  # 超出范围重新开始
        data_index = 0
    buffer.extend(data[data_index:data_index + span]) 
    data_index += span
    for i in range(batch_size // num_skips):  # 整除向下取整 是每个batch中中心词个数
        context_words = [w for w in range(span) if w != skip_window]  # [0,1,2,4,5,6]
        words_to_use = random.sample(context_words, num_skips)  # 从一个list中随机选取batch_size个元素(2)[a,b]
        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]
         #向前滑动窗口
        if data_index == len(data):#重新开始
            buffer.extend(data[0:span])
            data_index = span
        else:#向前滑动一个
            buffer.append(data[data_index])
            data_index += 1
    # Backtrack a little bit to avoid skipping words in the end of a batch
    data_index = (data_index + len(data) - span) % len(data)
    return batch, labels

样本生成图：

其中skip_window=2；num_skips=整个窗口大小，和上面代码有一点差异。

训练模型模块

def train():
    s=time.time()
    print("加载数据....")
    data=read_file("./序列标注文件.txt") #这个函数需要根据自己的数据重写
    print("cost:%s"%str(time.time()-s))
    
    s = time.time()
    print("创建字典...")
    data_id, word2id, id2word, word2count, vocabulary_size=make_vocabulary(data)
    print("cost:%s" % str(time.time() - s))

    print("保存词汇表...")
    print("词汇表大小: %d"%vocabulary_size)
    with codecs.open("./vocabulary_text", "w", "utf8") as fout:
        L = sorted(list(word2id.items()), key=lambda x: int(x[1]))
        for word,id in L:
            fout.write("%s\t%s\n"%(word,str(id)))

    # Input data
    X = tf.placeholder(tf.int32, shape=[None])
    # Input label
    Y = tf.placeholder(tf.int32, shape=[None, 1])

    # Create the embedding variable (each row represent a word embedding vector)
    embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))
    # Lookup the corresponding embedding vectors for each sample in X
    X_embed = tf.nn.embedding_lookup(embedding, X)

    # Construct the variables for the NCE loss
    nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))
    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

    #这就是tensorflow中Negative Sampling(负采样)的实现 
    #可以直接看作一个softmax层的近似
    loss_op = 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))

    # Define the optimizer
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    train_op = optimizer.minimize(loss_op)

    # Evaluation
    # Compute the cosine similarity between input data embedding and every embedding vectors
    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))
    cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True)

    # Initialize the variables (i.e. assign their default value)
    init = tf.global_variables_initializer()

    config = tf.ConfigProto()
    config.allow_soft_placement=True
    #可以获取到 operations 和 Tensor 被指派到哪个设备(几号CPU或几号GPU)上运行,
    # 会在终端打印出各项操作是在哪个设备上运行的。
    config.allow_soft_placement=True
    #如果将这个选项设置成True，那么当运行设备不满足要求时，会自动分配GPU或者CPU。
    config.gpu_options.per_process_gpu_memory_fraction = 0.5
    #当使用GPU时，设置GPU内存使用最大比例
    with tf.Session(config=config) as sess:
        # Run the initializer
        sess.run(init)

        # Testing data
        x_test = eval_words
        print(x_test)
        average_loss = 0
        s=time.time()
        for step in xrange(1, num_steps + 1):
            # Get a new batch of data
            batch_x, batch_y = next_batch(batch_size, num_skips, skip_window,data_id)
            # Run training op
            _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})
            average_loss += loss

            if step % display_step == 0 or step == 1:
                if step > 1:
                    average_loss /= display_step
                print("Step " + str(step) + ", Average Loss= " + \
                      "{:.4f}".format(average_loss)+"  cost: %s"%str(time.time()-s))
                average_loss = 0
                s = time.time()

            # Evaluation
            if step % eval_step == 0 or step == 1:
                print("Evaluation...")
                sim = sess.run(cosine_sim_op, feed_dict={X: x_test})
                for i in xrange(len(eval_words)):
                    top_k = 8  # number of nearest neighbors
                    nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                    log_str = '"%s" nearest neighbors:' % id2word[eval_words[i]]
                    for k in xrange(top_k):
                        log_str = '%s %s,' % (log_str, id2word[nearest[k]])
                    print(log_str)
        print("保存词向量...")
        embedding_array=np.array(sess.run(embedding),dtype=np.float32)
        np.save("./vocabulary_vec", embedding_array) #二进制保存
        np.savetxt("./vocabulary_vec.txt", embedding_array)#文本形式保存

完整代码

完整代码GitHub链接

Word2vec：skip-gram模型+Negative Sampling(负采样)代码实现

算法原理：

超参数

解释：

词典生成模块

batch生成模块

样本生成图：

训练模型模块

完整代码

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