tensorflow如何将one_hot标签和数字(整数)标签进行相互转化

import tensorflow as tf 
label = [1,0,2,3,0]
y = tf.one_hot(label, depth=4).numpy()    
#使用ont_hot返回的是tensor张量，再用numpy取出数组
print(y)

label = [np.argmax(i) for i in y] 
print（label）

import tensorflow as tf
tf.one_hot(indices, depth, on_value, off_value, axis)

import tensorflow as tf
# 得到4个5维独热行向量向量,
#    其中第1个向量的第0个分量是独热1，
#    第2个向量的第2个分量是独热，
#    第3个向量没有独热，因为指定为-1
#    第4个向量的第1个分量为独热
# labels向targets的转变
labels = [0, 2, -1, 1]
# labels是shape=(4,)的张量。则返回的targets是shape=(len(labels), depth)张量。
# 且这种情况下,axis=-1等价于axis=1
targets = tf.one_hot(indices=labels, depth=5, on_value=1.0, off_value=0.0, axis=-1)
with tf.Session() as sess:
    print(sess.run(targets))
[[ 1.  0.  0.  0.  0.]
 [ 0.  0.  1.  0.  0.]
 [ 0.  0.  0.  0.  0.]
 [ 0.  1.  0.  0.  0.]]
# 得到1个5维独热行向量。
targets = tf.one_hot(indices=3, depth=5, on_value=1.0, off_value=0.0, axis=0)
with tf.Session() as sess:
     print(sess.run(targets))
[ 0.  0.  0.  1.  0.]
# 得到1个5维独热列向量
targets = tf.one_hot(indices=[3], depth=5, on_value=1.0, off_value=0.0, axis=0)
with tf.Session() as sess:
     print(sess.run(targets))
[[ 0.]
[ 0.]
[ 0.]
[ 1.]
[ 0.]]
targets = tf.one_hot(indices=[[0,1],[1,0]], depth=3)
with tf.Session() as sess:
    print(sess.run(targets))
[[[ 1.  0.  0.]
  [ 0.  1.  0.]]
 [[ 0.  1.  0.]
  [ 1.  0.  0.]]]

# 得到分类的独热向量
targets = tf.one_hot(labels, num_classes)

import tensorflow as tf  # version : '1.12.0'
NUM_CLASSES = 10 # 10分类
labels = [0,1,2,3] # sample label
batch_size = tf.size(labels) # get size of labels : 4
labels = tf.expand_dims(labels, 1) # 增加一个维度
indices = tf.expand_dims(tf.range(0, batch_size,1), 1) #生成索引
concated = tf.concat([indices, labels] , 1) #作为拼接
onehot_labels = tf.sparse_to_dense(concated, tf.stack([batch_size, NUM_CLASSES]), 1.0, 0.0) # 生成one-hot编码的标签

from sklearn.preprocessing import OneHotEncoder,LabelEncoder
a = ['A','B','A','C']
label_encoder=LabelEncoder()
label_value = label_encoder.fit_transform(a)
enc = OneHotEncoder()
one_hot=enc.fit_transform(label_value.reshape(-1,1))
one_hot.toarray()
array([[1., 0., 0.],
       [0., 1., 0.],
       [1., 0., 0.],
       [0., 0., 1.]])

from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
one_hot = encoder.fit_transform(a)
print(one_hot)
array([[1, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 0, 1]])

import numpy as np
def dense_to_one_hot(labels_dense, num_classes):
    """Convert class labels from scalars to one-hot vectors."""
    num_labels = labels_dense.shape[0]
    index_offset = np.arange(num_labels) * num_classes
    labels_one_hot = np.zeros((num_labels, num_classes))
    labels_one_hot.flat[index_offset+labels_dense.ravel()] = 1
    return labels_one_hot
labels_dense = np.array([0,1,2,3,4]) 
num_classes  = 5
one_hot = dense_to_one_hot(labels_dense,num_classes)
print(one_hot)
[[1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1.]]

import numpy as np
from keras.utils import to_categorical
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 7]
data = array(data)
print(data)
# [1 2 3 4 5 6 7 8 9 7]
#有普通np数组转换为one-hot
one_hots = to_categorical(data)
print(one_hots)
# [[ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]
#  [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]
#  [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]
#  [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]
#  [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]
#  [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]
#  [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]
#  [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]
#  [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]
#  [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]]

# 由one-hot转换为普通np数组
data = [argmax(one_hot)for one_hot in one_hots]
print(data)
# [1, 2, 3, 4, 5, 6, 7, 8, 9, 7]