【TensorFlow】tf.nn.embedding

tf.nn.embedding_lookup()函数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，其他的参数不介绍。

ids为一维：

import tensorflow as tf import numpy as np #c = np.random.random([10, 1]) # 随机生成一个10*1的数组 #b = tf.nn.embedding_lookup(c, [1, 3])#查找数组中的序号为1和3的 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))

输出：

# 输出张量的第一和第三个元素 [[0.76574045] [0.71320516]] [[-0.38947418] [ 0.76574045] [ 1.485427 ] [ 0.71320516] [-1.6263497 ] [ 1.5424377 ] [-1.3040632 ] [ 1.2646209 ] [-1.0110979 ] [ 0.23503022]] <tf.Variable 'Variable:0' shape=(10, 1) dtype=float32_ref> <class 'tensorflow.python.ops.variables.RefVariable'>

ids是多维：

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_1/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_2/Identity:0", shape=(2, 4, 3), dtype=float64)

params是数组：

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 2.1 3.1 1.1] Tensor("embedding_lookup_6/Reshape_1:0", shape=(4,), dtype=float32) ================== [[0.1 2.1 3.1 1.1] [4.1 0.1 2.1 2.1]] Tensor("embedding_lookup_7/Reshape_1:0", shape=(2, 4), dtype=float32)

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