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How to perform one-hot encoding using Keras

Educative Answers Team

One-hot encoding is the representation of categorical variables as binary vectors. In Python, there are several ways to perform one-hot encoding on categorical data:

  • manual conversion

  • using scikit-learn

  • using ​Keras

Let’s have a look at how one-hot encoding can be performed in Keras.

One-hot encoding in Keras

The Keras API provides a to_categorical() method that can be used to one-hot encode integer data. If the integer data represents all the possible values of the classes, then the to_categorical() method can be used directly; otherwise, the number of classes can be passed to the method as the num_classes parameter.

The code snippet below illustrates the usage of the to_categorical() method:

import numpy as np
from keras.utils import to_categorical
### Categorical data to be converted to numeric data
colors = ["red", "green", "yellow", "red", "blue"]

### Universal list of colors
total_colors = ["red", "green", "blue", "black", "yellow"]

### map each color to an integer
mapping = {}
for x in range(len(total_colors)):
  mapping[total_colors[x]] = x

# integer representation
for x in range(len(colors)):
  colors[x] = mapping[colors[x]]

one_hot_encode = to_categorical(colors)
print(one_hot_encode)

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one hot encoding
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