深度學習-函數-tf.nn.embedding_lookup

關於np.random.RandomState、np.random.rand、np.random.random、np.random_sample參考https://blog.csdn.net/lanchunhui/article/details/50405670

tf.nn.embedding_lookup函數的用法主要是選取一個張量裏面索引對應的元素。tf.nn.embedding_lookup（params, ids）:params可以是張量也可以是數組等，id就是對應的索引，其他的參數不介紹。

例如：

ids只有一行

import tensorflow as tf
#c = np.random.random([10, 1])  # 隨機生成一個10*1的數組
#b = tf.nn.embedding_lookup(c, [1, 3])#查找數組中的序號爲1和3的
p=tf.Variable(tf.random_normal([10,1]))#生成10*1的張量
b = tf.nn.embedding_lookup(p, [1, 3])#查找張量中的序號爲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))

輸出：

[[0.15791859]
 [0.6468804 ]]
[[-0.2737084 ]
 [ 0.15791859]
 [-0.01315552]
 [ 0.6468804 ]
 [-1.4090979 ]
 [ 2.1583703 ]
 [ 1.4137447 ]
 [ 0.20688428]
 [-0.32815856]
 [-1.0601649 ]]
<tf.Variable 'Variable:0' shape=(10, 1) dtype=float32_ref>
<class 'tensorflow.python.ops.variables.Variable'>

分析：輸出爲張量的第一和第三個元素。

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

輸出：

[[ 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)

參考
https://www.cnblogs.com/gaofighting/p/9625868.html