关于这个接口，一直有疑惑，今天通过一个非常简单的例子简单理解了一下。

• 原型：tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None, validate_indices=True, max_norm=None)
• 在网上搜会发现基本都是假设ids只有一行，但是假如ids有若干行，会怎样？
• 直接上代码：

# -*- coding= utf-8 -*-
import tensorflow as tf
import numpy as np

a = [[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)
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 sess.run(out1)
    print out1
    print '=================='
    print sess.run(out2)
    print out2

输出：

[[ 0.1  0.2  0.3]
 [ 2.1  2.2  2.3]
 [ 3.1  3.2  3.3]
 [ 1.1  1.2  1.3]]
Tensor("embedding_lookup:0", shape=(4, 3), dtype=float64)
==================
[[[ 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]]]
Tensor("embedding_lookup_1:0", shape=(2, 4, 3), dtype=float64)