浅谈keras中的keras.utils.to_categorical用法
如下所示:
to_categorical(y, num_classes=None, dtype='float32')
将整型标签转为onehot。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。说出来显得复杂,请看下面实例。
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
补充知识:keras笔记——keras.utils.to_categoracal()函数
keras.utils.to_categoracal (y, num_classes=None, dtype='float32')
将整形标签转为onehot,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),否则为len(y)*num_classes。
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