keras中的keras.utils.to_categorical方法

• 2020 年 1 月 14 日
• 筆記

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。

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