python-kerasHow do I use the to_categorical function in Python Keras?

The to_categorical function in Python Keras is used to convert a class vector (integers) to binary class matrix. This is useful for using with categorical crossentropy loss function, which expects the labels to follow a binary encoding.

Example

from keras.utils import to_categorical

# define example
data = [1, 3, 2, 0, 3, 2, 2, 1, 0, 1]

# one hot encode
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.]]

The to_categorical function takes the following parameters:

• 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...)

The function returns a binary matrix representation of the input.

Helpful links

• Keras Documentation - to_categorical
• Towards Data Science - One Hot Encoding with Keras

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