tensorflow——tf.one_hot以及tf.sparse_to_dense函数

1、tf.one_hot函数

import numpy as npimport tensorflow as tfSIZE=6CLASS=10label1=np.random.randint(0,10,size=SIZE) b = tf.one_hot(label1,CLASS,1,0)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    sess.run(b)    print(sess.run(b))

输出结果：
产生的随机数：[7, 2, 9, 8, 4, 2]

[[ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.] [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.] [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.] [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]]

2、tf.sparse_to_dense函数

import tensorflow as tf   import numpy as npSIZE=6CLASS=10label=np.random.randint(0,10,size=SIZE) label=np.reshape(label,[SIZE,1])index = np.reshape(np.arange(SIZE), [SIZE, 1])#use a matrix  concated = tf.concat([index, label], 1)  onehot_labels = tf.sparse_to_dense(concated, [SIZE, CLASS], 1.0, 0.0)  #use a vector  concated2=tf.constant([1,3,4])  onehot_labels2 = tf.sparse_to_dense(concated2, [ CLASS], 1.0, 0.0)#use a scalar  concated3=tf.constant(5)  onehot_labels3 = tf.sparse_to_dense(concated3, [ CLASS], 1.0, 0.0)  with tf.Session() as sess:      sess.run(tf.global_variables_initializer())    result1=sess.run(onehot_labels)      result2 = sess.run(onehot_labels2)      result3 = sess.run(onehot_labels3)      print ("This is result1:")      print (result1)      print ("This is result2:")      print (result2)      print ("This is result3:")      print (result3) 

输出结果：
产生的随机数：[7, 2, 9, 8, 4, 2]

This is result1:[[ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.] [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.] [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.] [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]]This is result2:[ 0.  1.  0.  1.  1.  0.  0.  0.  0.  0.]This is result3:[ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]