参考链接：https://blog.csdn.net/nima1994/article/details/82468965
参考链接：https://blog.csdn.net/gdl3463315/article/details/82659378
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
-
ohl=keras.utils.to_categorical([1,3])
-
# ohl=keras.utils.to_categorical([[1],[3]])
-
print(ohl)
-
"""
-
[[0. 1. 0. 0.]
-
[0. 0. 0. 1.]]
-
"""
-
ohl=keras.utils.to_categorical([1,3],num_classes=5)
-
print(ohl)
-
"""
-
[[0. 1. 0. 0. 0.]
-
[0. 0. 0. 1. 0.]]
-
"""
该部分keras源码如下：
-
def to_categorical(y, num_classes=None, dtype=‘float32‘):
-
"""Converts a class vector (integers) to binary class matrix.
-
E.g. for use with categorical_crossentropy.
-
# Arguments
-
y: class vector to be converted into a matrix
-
(integers from 0 to num_classes).
-
num_classes: total number of classes.
-
dtype: The data type expected by the input, as a string
-
(`float32`, `float64`, `int32`...)
-
# Returns
-
A binary matrix representation of the input. The classes axis
-
is placed last.
-
"""
-
y = np.array(y, dtype=‘int‘)
-
input_shape = y.shape
-
if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
-
input_shape = tuple(input_shape[:-1])
-
y = y.ravel()
-
if not num_classes:
-
num_classes = np.max(y) + 1
-
n = y.shape[0]
-
categorical = np.zeros((n, num_classes), dtype=dtype)
-
categorical[np.arange(n), y] = 1
-
output_shape = input_shape + (num_classes,)
-
categorical = np.reshape(categorical, output_shape)
-
return categorical
简单来说：**keras.utils.to_categorical函数：是把类别标签转换为onehot编码（categorical就是类别标签的意思，表示现实世界中你分类的各类别）， 而onehot编码是一种方便计算机处理的二元编码。**
原文地址：https://www.cnblogs.com/klausage/p/12309823.html