张量流中word2vec负采样实现的理解问题

Question

https://github.com/tensorflow/models/blob/master/tutorials/embedding/word2vec.py 作为此代码中word2vec的实现，在生成样本时，注释显示为“负采样”，但在接下来的损耗计算部分中，注释显示为“ nce_loss”。据我所知，根据代码，应该是“负采样”，而不是“ nce”或“ nce_loss”。谁能帮忙解释一下？

  # Negative sampling.
    sampled_ids, _, _ = (tf.nn.fixed_unigram_candidate_sampler(
        true_classes=labels_matrix,
        num_true=1,
        num_sampled=opts.num_samples,
        unique=True,
        range_max=opts.vocab_size,
        distortion=0.75,
        unigrams=opts.vocab_counts.tolist()))

    # Embeddings for examples: [batch_size, emb_dim]
    example_emb = tf.nn.embedding_lookup(emb, examples)

    # Weights for labels: [batch_size, emb_dim]
    true_w = tf.nn.embedding_lookup(sm_w_t, labels)
    # Biases for labels: [batch_size, 1]
    true_b = tf.nn.embedding_lookup(sm_b, labels)

    # Weights for sampled ids: [num_sampled, emb_dim]
    sampled_w = tf.nn.embedding_lookup(sm_w_t, sampled_ids)
    # Biases for sampled ids: [num_sampled, 1]
    sampled_b = tf.nn.embedding_lookup(sm_b, sampled_ids)

    # True logits: [batch_size, 1]
    true_logits = tf.reduce_sum(tf.multiply(example_emb, true_w), 1) + true_b

    # Sampled logits: [batch_size, num_sampled]
    # We replicate sampled noise labels for all examples in the batch
    # using the matmul.
    sampled_b_vec = tf.reshape(sampled_b, [opts.num_samples])
    sampled_logits = tf.matmul(example_emb,
                               sampled_w,
                               transpose_b=True) + sampled_b_vec
    return true_logits, sampled_logits

和损失

  def nce_loss(self, true_logits, sampled_logits):
    """Build the graph for the NCE loss."""

    # cross-entropy(logits, labels)
    opts = self._options
    true_xent = tf.nn.sigmoid_cross_entropy_with_logits(
        labels=tf.ones_like(true_logits), logits=true_logits)
    sampled_xent = tf.nn.sigmoid_cross_entropy_with_logits(
        labels=tf.zeros_like(sampled_logits), logits=sampled_logits)

    # NCE-loss is the sum of the true and noise (sampled words)
    # contributions, averaged over the batch.
    nce_loss_tensor = (tf.reduce_sum(true_xent) +
                       tf.reduce_sum(sampled_xent)) / opts.batch_size
    return nce_loss_tensor