tf.keras.backend.categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

Aliases:

``````tf.keras.backend.categorical_crossentropy(
target,
output,
from_logits=False,
axis=-1
)
``````

Arguments:

• `target`: A tensor of the same shape as `output`.
• `output`: A tensor resulting from a softmax (unless `from_logits` is True, in which case `output` is expected to be the logits).
• `from_logits`: Boolean, whether `output` is the result of a softmax, or is a tensor of logits.
• `axis`: Int specifying the channels axis. `axis=-1` corresponds to data format `channels_last', and`axis=1`corresponds to data format`channels_first`.

Output tensor.

Raises:

• `ValueError`: if `axis` is neither -1 nor one of the axes of `output`.

Example:

``````import tensorflow as tf
from tensorflow.keras import backend as K
a = tf.constant([1., 0., 0., 0., 1., 0., 0., 0., 1.], shape=[3,3])
print("a: ", a)
b = tf.constant([.9, .05, .05, .5, .89, .6, .05, .01, .94], shape=[3,3])
print("b: ", b)
loss = K.categorical_crossentropy(a, b)
print('Loss: ', loss) #Loss: tf.Tensor([0.10536055 0.8046684  0.06187541], shape=(3,), dtype=float32)
loss = K.categorical_crossentropy(a, a)
print('Loss: ', loss) #Loss:  tf.Tensor([1.1920929e-07 1.1920929e-07 1.1920929e-07], shape=(3,), dtype=float32)
``````