|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/losses/losses.py#L2148-L2208) | Computes the categorical crossentropy loss. #### View aliases **Main aliases** [`tf.keras.metrics.categorical_crossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_crossentropy)
tf.keras.losses.categorical_crossentropy( y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1 )


| ## Args || |---|---| | `y_true` | Tensor of one-hot true targets. | | `y_pred` | Tensor of predicted targets. | | `from_logits` | Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. | | `label_smoothing` | Float in \[0, 1\]. If \> `0` then smooth the labels. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. | | `axis` | Defaults to `-1`. The dimension along which the entropy is computed. |



| ## Returns || |---|---| | Categorical crossentropy loss value. ||
#### Example: y_true = [[0, 1, 0], [0, 0, 1]] y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]] loss = keras.losses.categorical_crossentropy(y_true, y_pred) assert loss.shape == (2,) loss array([0.0513, 2.303], dtype=float32)