tf.keras.ops.categorical_crossentropy

Computes categorical cross-entropy loss between target and output tensor.

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

The categorical cross-entropy loss is commonly used in multi-class classification tasks where each input sample can belong to one of multiple classes. It measures the dissimilarity between the target and output probabilities or logits.

target The target tensor representing the true categorical labels. Its shape should match the shape of the output tensor except for the last dimension.
output The output tensor representing the predicted probabilities or logits. Its shape should match the shape of the target tensor except for the last dimension.
from_logits (optional) Whether output is a tensor of logits or probabilities. Set it to True if output represents logits; otherwise, set it to False if output represents probabilities. Defaults toFalse.
axis (optional) The axis along which the categorical cross-entropy is computed. Defaults to -1, which corresponds to the last dimension of the tensors.

Integer tensor: The computed categorical cross-entropy loss between target and output.

Example:

target = keras.ops.convert_to_tensor(
[[1, 0, 0],
 [0, 1, 0],
 [0, 0, 1]])
output = keras.ops.convert_to_tensor(
[[0.9, 0.05, 0.05],
 [0.1, 0.8, 0.1],
 [0.2, 0.3, 0.5]])
categorical_crossentropy(target, output)
array([0.10536054 0.22314355 0.6931472 ], shape=(3,), dtype=float32)