|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/losses/losses.py#L2374-L2417) | Computes the binary crossentropy loss. #### View aliases **Main aliases** [`tf.keras.metrics.binary_crossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/binary_crossentropy)
tf.keras.losses.binary_crossentropy( y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1 ) ### Used in the notebooks | Used in the tutorials | |---| | - [Wiki Talk Comments Toxicity Prediction](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_TFCO_Wiki_Case_Study) |


| ## Args || |---|---| | `y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]`. | | `y_pred` | The predicted values. shape = `[batch_size, d0, .. dN]`. | | `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 by squeezing them towards 0.5, that is, using `1. - 0.5 * label_smoothing` for the target class and `0.5 * label_smoothing` for the non-target class. | | `axis` | The axis along which the mean is computed. Defaults to `-1`. |



| ## Returns || |---|---| | Binary crossentropy loss value. shape = `[batch_size, d0, .. dN-1]`. ||
#### Example: y_true = [[0, 1], [0, 0]] y_pred = [[0.6, 0.4], [0.4, 0.6]] loss = keras.losses.binary_crossentropy(y_true, y_pred) assert loss.shape == (2,) loss array([0.916 , 0.714], dtype=float32)