python-kerasHow do I use the to_categorical function from TensorFlow in Python to convert data into a format suitable for a neural network?

The to_categorical function from TensorFlow in Python is used to convert data into a format suitable for a neural network. It is used to convert an array of numerical values into a binary matrix, where each row corresponds to one of the possible categories.

For example, the following code snippet converts a list of labels into a binary matrix:

import tensorflow as tf

labels = [0, 1, 2, 3]

binary_matrix = tf.keras.utils.to_categorical(labels)

print(binary_matrix)

Output example

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

The code consists of the following parts:

1. Importing the tensorflow library as tf.
2. Creating a list of labels.
3. Using the to_categorical function to convert the labels into a binary matrix.
4. Printing the binary matrix.

The to_categorical function is useful for creating training data for a neural network. It is a convenient way to convert numerical values into a binary matrix, which can be used by the neural network to classify data.

Helpful links

• https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical
• https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/

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