import numpy as np
import tensorflow as tf


indices = [0, 1, 1]  # rank=1
depth = 8
a = tf.one_hot(indices, depth)  # rank=2,输出为[3,3]

indices=[0,2,-2,1]  #rank=1
depth=7
b=tf.one_hot(indices,depth,on_value=5.0,off_value=1.0,axis=-1)

print(a)
print(b)


结果：
tf.Tensor(
[[1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0.]], shape=(3, 8), dtype=float32)
tf.Tensor(
[[5. 1. 1. 1. 1. 1. 1.]
 [1. 1. 5. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1.]
 [1. 5. 1. 1. 1. 1. 1.]], shape=(4, 7), dtype=float32)
2022-01-26 23:03:21.608213: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2181e053ce0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2022-01-26 23:03:21.608562: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2022-01-26 23:03:21.608979: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-01-26 23:03:21.609264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      

Process finished with exit code 0

one_hot(a,b)中，b代表向量的长度

                           a为列表，代表每个行向量中主元素（on_value）的值为1（若设定，则为设定值），其余元素（off_value）的值为0（若设定，则为设定值）。