Improved error message for missing class_weights. (keras-team#7238)

The error is now a ValueError, as per docs, and contains
a suitable message.

keras/engine/training.py

@@ -579,7 +579,18 @@ def _standardize_weights(y, sample_weight=None, class_weight=None,
             y_classes = np.reshape(y, y.shape[0])
         else:
             y_classes = y
-        weights = np.asarray([class_weight[cls] for cls in y_classes])
+
+        weights = np.asarray([class_weight[cls] for cls in y_classes
+                              if cls in class_weight])
+
+        if len(weights) != len(y_classes):
+            # subtract the sets to pick all missing classes
+            existing_classes = set(y_classes)
+            existing_class_weight = set(class_weight.keys())
+            raise ValueError('`class_weight` must contain all classes in the data.'
+                             ' The classes %s exist in the data but not in '
+                             '`class_weight`.'
+                             % (existing_classes - existing_class_weight))
         return weights
     else:
         if sample_weight_mode is None:

tests/test_loss_weighting.py

@@ -148,5 +148,20 @@ def test_sequential_temporal_sample_weights():
     assert(score < standard_score_sequential)
 
 
+@keras_test
+def test_class_weight_wrong_classes():
+    model = create_sequential_model()
+    model.compile(loss=loss, optimizer='rmsprop')
+
+    (x_train, y_train), (x_test, y_test), (sample_weight, class_weight, test_ids) = _get_test_data()
+
+    del class_weight[1]
+    try:
+        model.fit(x_train, y_train, epochs=0, verbose=0, class_weight=class_weight)
+        assert False
+    except ValueError:
+        pass  # expected behavior is to raise a ValueError with a suitable message
+
+
 if __name__ == '__main__':
     pytest.main([__file__])