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tf.keras.utils.to_categorical

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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. If None, this would be inferred as the (largest number in y) + 1.
dtype The data type expected by the input. Default: 'float32'.
Returns
A binary matrix representation of the input. The classes axis is placed last.

Example:

a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
a = tf.constant(a, shape=[4, 4])
print(a)
tf.Tensor(
  [[1. 0. 0. 0.]
   [0. 1. 0. 0.]
   [0. 0. 1. 0.]
   [0. 0. 0. 1.]], shape=(4, 4), dtype=float32)
b = tf.constant([.9, .04, .03, .03,
                 .3, .45, .15, .13,
                 .04, .01, .94, .05,
                 .12, .21, .5, .17],
                shape=[4, 4])
loss = tf.keras.backend.categorical_crossentropy(a, b)
print(np.around(loss, 5))
[0.10536 0.82807 0.1011  1.77196]
loss = tf.keras.backend.categorical_crossentropy(a, a)
print(np.around(loss, 5))
[0. 0. 0. 0.]
Raises
Value Error: If input contains string value

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/utils/to_categorical