tf.contrib.layers.embed_sequence

简介

一般用于sequence2sequence网络，可完成对输入序列数据的嵌入工作。一般只需给出前三个参数。

tf.contrib.layers.embed_sequence(ids, vocab_size,  embed_dim)

ids: 形状为[batch_size, doc_length]的int32或int64张量，也就是经过预处理的输入数据。

vocab_size: 输入数据的总词汇量，指的是总共有多少类词汇，不是总个数

embed_dim：想要得到的嵌入矩阵的维度

tensorflow官网原文

tf.contrib.layers.embed_sequence


embed_sequence(
    ids,
    vocab_size=None,
    embed_dim=None,
    unique=False,
    initializer=None,
    regularizer=None,
    trainable=True,
    scope=None,
    reuse=None
)
Defined in tensorflow/contrib/layers/python/layers/encoders.py.


See the guide: Layers (contrib) > Higher level ops for building neural network layers


Maps a sequence of symbols to a sequence of embeddings.


Typical use case would be reusing embeddings between an encoder and decoder.


Args:


ids: [batch_size, doc_length] Tensor of type int32 or int64 with symbol ids.
vocab_size: Integer number of symbols in vocabulary.
embed_dim: Integer number of dimensions for embedding matrix.
unique: If True, will first compute the unique set of indices, and then lookup each embedding once, repeating them in the output as needed.
initializer: An initializer for the embeddings, if None default for current scope is used.
regularizer: Optional regularizer for the embeddings.
trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
scope: Optional string specifying the variable scope for the op, required if reuse=True.
reuse: If True, variables inside the op will be reused.
Returns:


Tensor of [batch_size, doc_length, embed_dim] with embedded sequences.


Raises:


ValueError: if embed_dim or vocab_size are not specified when reuse is None or False.