keras.utils.to_categorical方法

用法：to_categorical(y, num_classes=None, dtype='float32')

将整型的类别标签转为onehot编码。y为int数组，num_classes为标签类别总数，大于max(y)（标签从0开始的）。

返回：如果num_classes=None，返回len(y) * [max(y)+1]（维度，m*n表示m行n列矩阵，下同），否则为len(y) * num_classes。

import keras
 
train=keras.utils.to_categorical([1,3])
print(train)
"""
[[0. 1. 0. 0.]
 [0. 0. 0. 1.]]
"""
train=keras.utils.to_categorical([1,3],num_classes=5)
print(train)
"""
[[0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0.]]
"""

将类别标签转化为one-hot编码，MNIST示例（十个类别: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9）：

y = np.array([1,4,5,9,3,5,7,0,3,6])

y = keras.utils.to_categorical(y, num_classes=10)

y	0	1	2	3	4	5	6	7	8	9
1	0	1	0	0	0	0	0	0	0	0
4	0	0	0	0	1	0	0	0	0	0
5	0	0	0	0	0	1	0	0	0	0
9	0	0	0	0	0	0	0	0	0	1
3	0	0	0	1	0	0	0	0	0	0
5	0	0	0	0	0	1	0	0	0	0
7	0	0	0	0	0	0	0	1	0	0
0	1	0	0	0	0	0	0	0	0	0
3	0	0	0	1	0	0	0	0	0	0
6	0	0	0	0	0	0	1	0	0	0

即：

y = [[0. 1. 0. 0. 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. 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. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]