Method add_loss

tensorflow/python/keras/engine/base_layer.py:1040–1157

Add loss tensor(s), potentially dependent on layer inputs. Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs `a` and `b`, some entries in `layer.losses` may be

(self, losses, inputs=None)

  @doc_controls.for_subclass_implementers
  def add_loss(self, losses, inputs=None):
    """Add loss tensor(s), potentially dependent on layer inputs.

    Some losses (for instance, activity regularization losses) may be dependent
    on the inputs passed when calling a layer. Hence, when reusing the same
    layer on different inputs `a` and `b`, some entries in `layer.losses` may
    be dependent on `a` and some on `b`. This method automatically keeps track
    of dependencies.

    This method can be used inside a subclassed layer or model's `call`
    function, in which case `losses` should be a Tensor or list of Tensors.

    Example:

    ```python
    class MyLayer(tf.keras.layers.Layer):
      def call(inputs, self):
        self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
        return inputs
    ```

    This method can also be called directly on a Functional Model during
    construction. In this case, any loss Tensors passed to this Model must
    be symbolic and be able to be traced back to the model's `Input`s. These
    losses become part of the model's topology and are tracked in `get_config`.

    Example:

    ```python
    inputs = tf.keras.Input(shape=(10,))
    x = tf.keras.layers.Dense(10)(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    # Actvity regularization.
    model.add_loss(tf.abs(tf.reduce_mean(x)))
    ```

    If this is not the case for your loss (if, for example, your loss references
    a `Variable` of one of the model's layers), you can wrap your loss in a
    zero-argument lambda. These losses are not tracked as part of the model's
    topology since they can't be serialized.

    Example:

    ```python
    inputs = tf.keras.Input(shape=(10,))
    x = tf.keras.layers.Dense(10)(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    # Weight regularization.
    model.add_loss(lambda: tf.reduce_mean(x.kernel))
    ```

    The `get_losses_for` method allows to retrieve the losses relevant to a
    specific set of inputs.

    Arguments:
      losses: Loss tensor, or list/tuple of tensors. Rather than tensors, losses