Improve error messages in data validation checks.

keras/engine/training.py

@@ -50,6 +50,8 @@ def _standardize_input_data(data, names, shapes=None,
     # Raises
         ValueError: in case of improperly formatted user-provided data.
     """
+    if not names:
+        return []
     if data is None:
         return [None for _ in range(len(names))]
     if isinstance(data, dict):
@@ -63,7 +65,8 @@ def _standardize_input_data(data, names, shapes=None,
     elif isinstance(data, list):
         if len(data) != len(names):
             if data and hasattr(data[0], 'shape'):
-                raise ValueError('Error when checking ' + exception_prefix +
+                raise ValueError('Error when checking model ' +
+                                 exception_prefix +
                                  ': the list of Numpy arrays '
                                  'that you are passing to your model '
                                  'is not the size the model expected. '
@@ -77,7 +80,8 @@ def _standardize_input_data(data, names, shapes=None,
                     data = [np.asarray(data)]
                 else:
                     raise ValueError(
-                        'Error when checking ' + exception_prefix +
+                        'Error when checking model ' +
+                        exception_prefix +
                         ': you are passing a list as '
                         'input to your model, '
                         'but the model expects '
@@ -88,15 +92,17 @@ def _standardize_input_data(data, names, shapes=None,
         arrays = data
     else:
         if not hasattr(data, 'shape'):
-            raise TypeError('Error when checking ' + exception_prefix +
+            raise TypeError('Error when checking model ' +
+                            exception_prefix +
                             ': data should be a Numpy array, '
                             'or list/dict of Numpy arrays. '
                             'Found: ' + str(data)[:200] + '...')
-        if len(names) != 1:
+        if len(names) > 1:
             # Case: model expects multiple inputs but only received
             # a single Numpy array.
             raise ValueError('The model expects ' + str(len(names)) +
-                             ' input arrays, but only received one array. '
+                             exception_prefix +
+                             ' arrays, but only received one array. '
                              'Found: array with shape ' + str(data.shape))
         arrays = [data]
 
@@ -1291,11 +1297,11 @@ class Model(Container):
         x = _standardize_input_data(x, self._feed_input_names,
                                     self._feed_input_shapes,
                                     check_batch_axis=False,
-                                    exception_prefix='model input')
+                                    exception_prefix='input')
         y = _standardize_input_data(y, self._feed_output_names,
                                     output_shapes,
                                     check_batch_axis=False,
-                                    exception_prefix='model target')
+                                    exception_prefix='target')
         sample_weights = _standardize_sample_weights(sample_weight,
                                                      self._feed_output_names)
         class_weights = _standardize_class_weights(class_weight,