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.
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
|
TensorFlow 1 version
View source on GitHub