# tf.keras.backend.categorical_crossentropy

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Categorical crossentropy between an output tensor and a target tensor.

### Aliases:

• tf.compat.v1.keras.backend.categorical_crossentropy
• tf.compat.v2.keras.backend.categorical_crossentropy
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', andaxis=1corresponds to data formatchannels_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)