message: Polish documentation.
diff: @@ -287,24 +287,25 @@ class MeanSquaredLogarithmicError(LossFunctionWrapper):
  @keras_export('keras.losses.BinaryCrossentropy')
  class BinaryCrossentropy(LossFunctionWrapper):
  - """Computes the crossentropy loss between the labels and predictions.
  + """Computes the cross-entropy loss between true labels and predicted labels.
  - Use this crossentropy loss function when there are only two label classes
  - (assumed to be 0 and 1). There should be a single floating point value per
  - feature.
  + Use this cross-entropy loss when there are only two label classes (assumed to
  + be 0 and 1). For each example, there should be a single floating-point value
  + per prediction.
  - In the snippet below, there is a single floating pointing value per example,
  - and the shape of both `y_pred` and `y_true` are `[batch_size]`.
  + In the snippet below, each of the four examples has only a single
  + floating-pointing value, and both `y_pred` and `y_true` have the shape
  + `[batch_size]`.
  Usage:
  ```python
  bce = tf.keras.losses.BinaryCrossentropy()
  loss = bce([0., 0., 1., 1.], [1., 1., 1., 0.])
  - print('Loss: ', loss.numpy()) # Loss: 12.007
  + print('Loss: ', loss.numpy()) # Loss: 11.522857
  ```
  - Usage with tf.keras API:
  + Usage with the `tf.keras` API:
  ```python
  model = tf.keras.Model(inputs, outputs)
  @@ -312,12 +313,18 @@ class BinaryCrossentropy(LossFunctionWrapper):
  ```
  Args:
  - 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.
  - reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
  - Default value is `SUM_OVER_BATCH_SIZE`.
  - name: Optional name for the op.
  + from_logits: Whether to interpret `y_pred` as a tensor of
  + [logit](https://en.wikipedia.org/wiki/Logit) values. By default, we assume
  + that `y_pred` contains probabilities (i.e., values in [0, 1]).
  + label_smoothing: Float in [0, 1]. When 0, no smoothing occurs. When > 0, we
  + compute the loss between the predicted labels and a smoothed version of
  + the true labels, where the smoothing squeezes the labels towards 0.5.
  + Larger values of `label_smoothing` correspond to heavier smoothing.
  + reduction: (Optional) The type of `tf.keras.losses.Reduction` to use to
  + combine the computed loss values for the individual examples into a single
  + loss value for the entire batch of examples. Defaults to
  + `SUM_OVER_BATCH_SIZE`.
  + name: (Optional) Name for the op.
  """
  def __init__(self,
  