tf.nn.embedding_lookup函数

tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None, validate_indices=True, max_norm=None)

简单的来讲，就是将一个数字序列ids转化成embedding序列表示。
假设params.shape=[v,h], ids.shape=[m], 那么该函数会返回一个shape=[m,h]的张量。
下面用一个例子演示其用法。

# -*- coding= utf-8 -*-import tensorflow as tfimport numpy as npa = [[0.1, 0.2, 0.3], [1.1, 1.2, 1.3], [2.1, 2.2, 2.3], [3.1, 3.2, 3.3], [4.1, 4.2, 4.3]]a = np.asarray(a)print('a:' , a)idx1 = tf.Variable([0, 2, 3, 1], tf.int32)idx2 = tf.Variable([[0, 2, 3, 1], [4, 0, 2, 2]], tf.int32)out1 = tf.nn.embedding_lookup(a, idx1)out2 = tf.nn.embedding_lookup(a, idx2)init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    print('out1:' , sess.run(out1))    print('out2:' , sess.run(out2))

输出结果：

[[ 0.1  0.2  0.3] [ 1.1  1.2  1.3] [ 2.1  2.2  2.3] [ 3.1  3.2  3.3] [ 4.1  4.2  4.3]]out1: [[ 0.1  0.2  0.3] [ 2.1  2.2  2.3] [ 3.1  3.2  3.3] [ 1.1  1.2  1.3]]out2: [[[ 0.1  0.2  0.3]  [ 2.1  2.2  2.3]  [ 3.1  3.2  3.3]  [ 1.1  1.2  1.3]] [[ 4.1  4.2  4.3]  [ 0.1  0.2  0.3]  [ 2.1  2.2  2.3]  [ 2.1  2.2  2.3]]]

从结果可以看出，embedding_lookup函数其实就是将矩阵按着给定的索引重新排序组成新的矩阵。