np_utils.to_categorical函数

np_utils.to_categorical

np_utils.to_categorical作用：

to_categorical()用于分类，将标签转为one-hot编码。

np_utils.to_categorical参数：

np_utils.to_categorical(y, num_classes)

参数:

• y:向量（数据的label），函数作用后，返回对应矩阵形式(从0到num_classes的整数)。

  • 若num_classes=3，则y应为[0,1,2]

• num_classes:种类的总数。如果是’ None '，则自动推断作为(y中最大的数)+ 1。

• dtype:数据类型。默认值:“float32”。

np_utils.to_categorical函数原型：

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. If `None`, this would be inferred
        as the (largest number in `y`) + 1.
      dtype: The data type expected by the input. Default: `'float32'`.

  Returns:
      A binary matrix representation of the input. The classes axis is placed
      last.

  Example:

  >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
  >>> a = tf.constant(a, shape=[4, 4])
  >>> print(a)
  tf.Tensor(
    [[1. 0. 0. 0.]
     [0. 1. 0. 0.]
     [0. 0. 1. 0.]
     [0. 0. 0. 1.]], shape=(4, 4), dtype=float32)

  >>> b = tf.constant([.9, .04, .03, .03,
  ...                  .3, .45, .15, .13,
  ...                  .04, .01, .94, .05,
  ...                  .12, .21, .5, .17],
  ...                 shape=[4, 4])
  >>> loss = tf.keras.backend.categorical_crossentropy(a, b)
  >>> print(np.around(loss, 5))
  [0.10536 0.82807 0.1011  1.77196]

  >>> loss = tf.keras.backend.categorical_crossentropy(a, a)
  >>> print(np.around(loss, 5))
  [0. 0. 0. 0.]

  Raises:
      Value Error: If input contains string value

  """
  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