tf.nn.embedding_lookup函数的用法

tf.nn.embedding_lookup函数的用法主要是选取一个张量里面索引对应的元素。tf.nn.embedding_lookup（params, ids）:params可以是张量也可以是数组等，id就是对应的索引

ids只有一行：

p=tf.Variable(tf.random_normal([10,1]))#生成10*1的张量
b = tf.nn.embedding_lookup(p, [1, 3])#查找张量中的序号为1和3的
 
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(b))
    #print(c)
    print(sess.run(p))
    print(p)
    print(type(p))
    

[[-0.6743774 ]
 [ 0.03631003]]
[[-0.06934407]
 [-0.6743774 ]
 [ 0.3789335 ]
 [ 0.03631003]
 [ 0.6917214 ]
 [-1.0486908 ]
 [ 2.2114675 ]
 [ 1.096658  ]
 [ 0.17109118]
 [ 0.78292763]]
<tf.Variable 'Variable_4:0' shape=(10, 1) dtype=float32_ref>
<class 'tensorflow.python.ops.variables.RefVariable'>

如果ids是多行：

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)

[[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_5/Identity: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_6/Identity:0", shape=(2, 4, 3), dtype=float64)