tf.keras.utils.to_categorical

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

tf.keras.utils.to_categorical(
    y, num_classes=None, dtype='float32'
)

E.g. for use with categorical_crossentropy.

Args
`y`	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(y) + 1`.
`dtype`	The data type expected by the input. Default: `'float32'`.

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

Example:

a = tf.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 = 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.]

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Last updated 2023-10-06 UTC.

tf.keras.utils.to_categorical

Args

Returns

Example: