Fixed layer.losses such that it recurses into sublayer losses (#18828)

* Fixed layer.losses such that it recurses into sublayer losses

* moved recursion into sub function

* moved recursion to losses property

keras/layers/layer.py

@@ -1073,9 +1073,9 @@ def _get_own_losses(self):
 
     @property
     def losses(self):
-        """List of scalar losses added via `add_loss()` during layer call."""
+        """List of scalar losses from `add_loss`, regularizers and sublayers."""
         losses = self._get_own_losses()
-        for layer in self._layers:
+        for layer in self._flatten_layers(include_self=False):
             losses.extend(layer._get_own_losses())
         weight_regularization_losses = []
         for v in self.trainable_weights:

keras/layers/layer_test.py

@@ -328,6 +328,12 @@ def call(self, x):
         self.assertLen(model.losses, 1)
         self.assertAllClose(model.losses[0], 1.0)
 
+        # It works recursively in nested models
+        model = models.Sequential([model])
+        model(np.ones((1,)))
+        self.assertLen(model.losses, 1)
+        self.assertAllClose(model.losses[0], 1.0)
+
     def test_training_arg_value_resolution(self):
         # Check that even if `training` is not passed
         # to an inner layer, the outer value gets propagated