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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。

以上这篇浅谈keras中的keras.utils.to_categorical用法就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。