helpers.py 文件源码

def sample_and_embed(embedding, temperature, output_list=None,
                     output_projection=None):
    """Returns a callable (usable as a loop_fn for seq2seq) which takes a
    sample from a batch of outputs and embeds them. Optionally applies a
    projection first.

    Args:
        embedding: an embedding matrix to lookup symbols in.
        temperature: temperature to control the pointiness of the softmax.
        output_list (Optional): a list in which to collect the samples.
            Default None means don't collect them at all.
        output_proj (Optional): tuple (weight, biases) used to project outputs.
            If None (default), no projection is performed.

    Returns:
        embedding from embedding.
    """
    def _sample_embed(prev, _):
        var = _maybe_project(prev, output_projection)
        var /= temperature

        next_ = tf.multinomial(var, 1)
        # get rid of the num_samples dimension
        next_ = tf.squeeze(next_)
        # maybe store it
        if output_list is not None:
            output_list.append(next_)
        # look up the embedding
        next_ = tf.nn.embedding_lookup(
            embedding, next_)

        return next_

    return _sample_embed