tf.keras.utils.to_categorical

Converts a class vector (integers) to binary class matrix.

tf.keras.utils.to_categorical(
    x, num_classes=None
)

Used in the notebooks

Used in the tutorials
Implement Differential Privacy with TensorFlow Privacy Assess privacy risks with the TensorFlow Privacy Report On-Device Training with TensorFlow Lite Human Pose Classification with MoveNet and TensorFlow Lite Classifying CIFAR-10 with XLA

E.g. for use with categorical_crossentropy.

Args
x	Array-like with class values to be converted into a matrix (integers from 0 to num_classes - 1).
num_classes	Total number of classes. If None, this would be inferred as max(x) + 1. Defaults to None.

Returns
A binary matrix representation of the input as a NumPy array. The class axis is placed last.

Example:

a = keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
print(a)
[[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]]

b = np.array([.9, .04, .03, .03,
              .3, .45, .15, .13,
              .04, .01, .94, .05,
              .12, .21, .5, .17],
              shape=[4, 4])
loss = keras.ops.categorical_crossentropy(a, b)
print(np.around(loss, 5))
[0.10536 0.82807 0.1011  1.77196]

loss = keras.ops.categorical_crossentropy(a, a)
print(np.around(loss, 5))
[0. 0. 0. 0.]