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.]]
"""

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