keras中to_categorical函数解析
1.to_categorical的功能
简单来说,to_categorical就是将类别向量转换为二进制(只有0和1)的矩阵类型表示。其表现为将原有的类别向量转换为独热编码的形式。先上代码看一下效果:
from keras.utils.np_utils import * #类别向量定义 b = [0,1,2,3,4,5,6,7,8] #调用to_categorical将b按照9个类别来进行转换 b = to_categorical(b, 9) print(b) 执行结果如下: [[1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1.]]
to_categorical最为keras中提供的一个工具方法,从以上代码运行可以看出,将原来类别向量中的每个值都转换为矩阵里的一个行向量,从左到右依次是0,1,2,...8个类别。2表示为[0. 0. 1. 0. 0. 0. 0. 0. 0.],只有第3个为1,作为有效位,其余全部为0。
2.one_hot encoding(独热编码)介绍
独热编码又称为一位有效位编码,上边代码例子中其实就是将类别向量转换为独热编码的类别矩阵。也就是如下转换:
0 1 2 3 4 5 6 7 8 0=> [1. 0. 0. 0. 0. 0. 0. 0. 0.] 1=> [0. 1. 0. 0. 0. 0. 0. 0. 0.] 2=> [0. 0. 1. 0. 0. 0. 0. 0. 0.] 3=> [0. 0. 0. 1. 0. 0. 0. 0. 0.] 4=> [0. 0. 0. 0. 1. 0. 0. 0. 0.] 5=> [0. 0. 0. 0. 0. 1. 0. 0. 0.] 6=> [0. 0. 0. 0. 0. 0. 1. 0. 0.] 7=> [0. 0. 0. 0. 0. 0. 0. 1. 0.] 8=> [0. 0. 0. 0. 0. 0. 0. 0. 1.]
那么一道思考题来了,让你自己编码实现类别向量向独热编码的转换,该怎样实现呢?
以下是我自己粗浅写的一个小例子,仅供参考:
def convert_to_one_hot(labels, num_classes): #计算向量有多少行 num_labels = len(labels) #生成值全为0的独热编码的矩阵 labels_one_hot = np.zeros((num_labels, num_classes)) #计算向量中每个类别值在最终生成的矩阵“压扁”后的向量里的位置 index_offset = np.arange(num_labels) * num_classes #遍历矩阵,为每个类别的位置填充1 labels_one_hot.flat[index_offset + labels] = 1 return labels_one_hot #进行测试 b = [2, 4, 6, 8, 6, 2, 3, 7] print(convert_to_one_hot(b,9)) 测试结果: [[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. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0.]]
3.源码解析
to_categorical在keras的utils/np_utils.py中,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. # Example ```python # Consider an array of 5 labels out of a set of 3 classes {0, 1, 2}: > labels array([0, 2, 1, 2, 0]) # `to_categorical` converts this into a matrix with as many # columns as there are classes. The number of rows # stays the same. > to_categorical(labels) array([[ 1., 0., 0.], [ 0., 0., 1.], [ 0., 1., 0.], [ 0., 0., 1.], [ 1., 0., 0.]], dtype=float32) ``` """ #将输入y向量转换为数组 y = np.array(y, dtype='int') #获取数组的行列大小 input_shape = y.shape if input_shape and input_shape[-1] == 1 and len(input_shape) > 1: input_shape = tuple(input_shape[:-1]) #y变为1维数组 y = y.ravel() #如果用户没有输入分类个数,则自行计算分类个数 if not num_classes: num_classes = np.max(y) + 1 n = y.shape[0] #生成全为0的n行num_classes列的值全为0的矩阵 categorical = np.zeros((n, num_classes), dtype=dtype) #np.arange(n)得到每个行的位置值,y里边则是每个列的位置值 categorical[np.arange(n), y] = 1 #进行reshape矫正 output_shape = input_shape + (num_classes,) categorical = np.reshape(categorical, output_shape) return categorical
参考链接:https://blog.csdn.net/moyu123456789/article/details/83444140