training.py _weighted_masked_objective fix crash when weights is None

keras/engine/training.py

@@ -444,13 +444,12 @@ def weighted(y_true, y_pred, weights, mask=None):
             #  to the number of unmasked samples.
             score_array /= K.mean(mask)
 
-        # reduce score_array to same ndim as weight array
-        ndim = K.ndim(score_array)
-        weight_ndim = K.ndim(weights)
-        score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
-
         # apply sample weighting
         if weights is not None:
+            # reduce score_array to same ndim as weight array
+            ndim = K.ndim(score_array)
+            weight_ndim = K.ndim(weights)
+            score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
             score_array *= weights
             score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
         return K.mean(score_array)

tests/keras/engine/test_training.py

@@ -6,7 +6,9 @@
 
 from keras.layers import Dense, Dropout
 from keras.engine.topology import Input
-from keras.engine.training import Model, _check_loss_and_target_compatibility
+from keras.engine.training import Model
+from keras.engine.training import _check_loss_and_target_compatibility
+from keras.engine.training import _weighted_masked_objective
 from keras.models import Sequential
 from keras import backend as K
 from keras.utils import Sequence
@@ -27,6 +29,18 @@ def __getitem__(self, idx):
             np.random.random((self.batch_size, 3))]
 
 
+@keras_test
+def test_weighted_masked_objective():
+    a = Input(shape=(3,), name='input_a')
+
+    # weighted_masked_objective
+    def mask_dummy(y_true=None, y_pred=None, weight=None):
+        return K.placeholder(y_true.shape)
+
+    weighted_function = _weighted_masked_objective(K.categorical_crossentropy)
+    weighted_function(a, a, None)
+
+
 @keras_test
 def test_model_methods():
     a = Input(shape=(3,), name='input_a')