tf.nn.embedding_lookup函數的用法主要是選取一個張量里面索引對應的元素。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

ids只有一行：

p=tf.Variable(tf.random_normal([10,1]))#生成10*1的張量 b = tf.nn.embedding_lookup(p, [1, 3])#查找張量中的序號為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 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) 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)

總結

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