Allow inputs of model training/testing methods to be a list of int/fl…

…oat values.

keras/engine/training.py

@@ -60,17 +60,19 @@ def _standardize_input_data(data, names, shapes=None,
     if isinstance(data, dict):
         try:
             data = [data[x].values if data[x].__class__.__name__ == 'DataFrame' else data[x] for x in names]
-            data = [np.expand_dims(x, 1) if x.ndim == 1 else x for x in data]
         except KeyError as e:
             raise ValueError(
                 'No data provided for "' + e.args[0] + '". Need data '
                 'for each key in: ' + str(names))
     elif isinstance(data, list):
-        data = [x.values if x.__class__.__name__ == 'DataFrame' else x for x in data]
-        data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
+        if len(names) == 1 and data and isinstance(data[0], (float, int)):
+            data = [np.asarray(data)]
+        else:
+            data = [x.values if x.__class__.__name__ == 'DataFrame' else x for x in data]
     else:
         data = data.values if data.__class__.__name__ == 'DataFrame' else data
-        data = [np.expand_dims(data, 1)] if data.ndim == 1 else [data]
+        data = [data]
+    data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
 
     if len(data) != len(names):
         if data and hasattr(data[0], 'shape'):

tests/keras/engine/test_training.py

@@ -568,6 +568,17 @@ def test_trainable_argument():
     assert_allclose(out, out_2)
 
 
+@keras_test
+def test_with_list_as_targets():
+    model = Sequential()
+    model.add(Dense(1, input_dim=3, trainable=False))
+    model.compile('rmsprop', 'mse')
+
+    x = np.random.random((2, 3))
+    y = [0, 1]
+    model.train_on_batch(x, y)
+
+
 @keras_test
 def test_check_not_failing():
     a = np.random.random((2, 1, 3))