Categorical crossentropy between an output tensor and a target tensor.
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=1corresponds to data format channels_first`.
|
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)