请给出一个使用TensorFlow实现的word2vec的详细代码示例。-DPDK中文网

以下是一个使用TensorFlow实现的word2vec的简单示例代码：

import tensorflow as tf

# 定义训练数据
sentences = [["I", "love", "tensorflow"], ["tensorflow", "is", "awesome"]]

# 构建词汇表
vocab = []
for sentence in sentences:
    vocab.extend(sentence)
vocab = list(set(vocab))

# 创建映射表
word2idx = {w: i for i, w in enumerate(vocab)}
idx2word = {i: w for i, w in enumerate(vocab)}

# 转换文本为索引序列
indexed_sentences = [[word2idx[word] for word in sentence] for sentence in sentences]

# 定义模型参数
embedding_dim = 128  # 词嵌入维度
window_size = 2      # 上下文窗口大小

# 定义占位符和变量
center_words = tf.placeholder(tf.int32, shape=[None])
target_words = tf.placeholder(tf.int32, shape=[None])
embeddings = tf.Variable(tf.random_uniform([len(vocab), embedding_dim], -1.0, 1.0))
softmax_weights = tf.Variable(tf.truncated_normal([len(vocab), embedding_dim], stddev=0.1))
softmax_biases = tf.Variable(tf.zeros(len(vocab)))

# 根据中心词预测上下文词的概率分布
center_embeds = tf.nn.embedding_lookup(embeddings, center_words)
loss_per_example = tf.nn.nce_loss(weights=softmax_weights,
                                  biases=softmax_biases,
                                  labels=target_words,
                                  inputs=center_embeds,
                                  num_sampled=len(vocab)//2,
                                  num_classes=len(vocab))
loss = tf.reduce_mean(loss_per_example)

# 定义优化器
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss)

# 开始训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(10):
        total_loss = 0
        for centers, targets in indexed_sentences:
            feed_dict = {center_words: centers, target_words: targets}
            _, batch_loss = sess.run([train_op, loss], feed_dict=feed_dict)
            total_loss += batch_loss
        print("Epoch:", epoch+1, "Loss:", total_loss)
    
    # 获取词向量矩阵
    trained_embeddings = embeddings.eval()

# 输出词向量结果
for i, embedding in enumerate(trained_embeddings):
    word = idx2word[i]
    print("Word:", word, "Embedding:", embedding)

以上代码演示了使用TensorFlow实现word2vec的基本步骤，包括数据准备、模型定义、优化器配置和训练过程。请注意，此示例仅用于说明目的，实际应用中可能需要更复杂的模型和调整参数来获得更好的效果。

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