python-kerasHow do I use keras.utils.to_categorical in Python?

Keras.utils.to_categorical is a utility function used to convert a numerical label vector (integers) to a binary class matrix. This is useful for working with a categorical data set in a neural network.

An example of using this function is shown below:

import numpy as np
from keras.utils import to_categorical

# define input data
data = np.array([1, 3, 2, 0, 3, 2, 2, 1, 0, 1])

# one hot encode the data
encoded = to_categorical(data)

print(encoded)

Output example

[[0. 1. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 1. 0.]
 [1. 0. 0. 0.]
 [0. 0. 0. 1.]
 [0. 0. 1. 0.]
 [0. 0. 1. 0.]
 [0. 1. 0. 0.]
 [1. 0. 0. 0.]
 [0. 1. 0. 0.]]

Code explanation

• import numpy as np: import numpy library for array operations.
• from keras.utils import to_categorical: import to_categorical function from keras.utils.
• data = np.array([1, 3, 2, 0, 3, 2, 2, 1, 0, 1]): define input data.
• encoded = to_categorical(data): one hot encode the data.
• print(encoded): print the encoded data.

Helpful links

• https://keras.io/utils/#to_categorical
• https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical

Edit this code on GitHub