Throw error if there are unexpected keys in sample_weight / class_weight dictionaries.

PiperOrigin-RevId: 241421418

tensorflow/python/keras/engine/training_test.py

@@ -1406,6 +1406,39 @@ class LossWeightingTest(keras_parameterized.TestCase):
             temporal_x_test[test_ids], temporal_y_test[test_ids], verbose=0)
         self.assertLess(score[0], ref_score[0])
 
+  @keras_parameterized.run_all_keras_modes
+  @keras_parameterized.run_with_all_model_types(exclude_models='sequential')
+  def test_fit_with_incorrect_weights(self):
+    input_a = keras.layers.Input(shape=(3,), name='input_a')
+    input_b = keras.layers.Input(shape=(3,), name='input_b')
+
+    dense = keras.layers.Dense(2, name='output_1')
+    dropout = keras.layers.Dropout(0.5, name='output_2')
+    branch_a = [input_a, dense]
+    branch_b = [input_b, dense, dropout]
+
+    model = testing_utils.get_multi_io_model(branch_a, branch_b)
+    model.compile(
+        optimizer='adam',
+        loss='mse',
+        run_eagerly=testing_utils.should_run_eagerly())
+    x = np.random.random((10, 3))
+    y = np.random.random((10, 2))
+
+    with self.assertRaisesRegexp(
+        ValueError,
+        r'Unknown entries in sample_weight dictionary: \[\'unknown\'\]. '
+        r'Only expected following keys: \[\'output_1\', \'output_2\'\]'):
+      model.fit([x, x], [y, y],
+                epochs=1,
+                sample_weight={'unknown': 'something'})
+
+    with self.assertRaisesRegexp(
+        ValueError,
+        r'Unknown entries in class_weight dictionary: \[\'unknown\'\]. '
+        r'Only expected following keys: \[\'output_1\', \'output_2\'\]'):
+      model.fit([x, x], [y, y], epochs=1, class_weight={'unknown': 'something'})
+
   @keras_parameterized.run_all_keras_modes
   def test_class_weight_invalid_use_case(self):
     num_classes = 5
@@ -2288,9 +2321,10 @@ class TestTrainingWithDataTensors(keras_parameterized.TestCase):
       output_b_np = np.random.random((10, 3))
 
       _ = model.train_on_batch([input_a_np, input_b_np],
-                               [output_a_np, output_b_np],
+                               [output_a_np, output_b_np], {
-                               {y: np.random.random((10, 4)),
+                                   'dense_1': np.random.random((10,)),
-                                y1: np.random.random((10, 3))})
+                                   'dropout': np.random.random((10,))
+                               })
       # test dictionary of target_tensors
       with self.assertRaises(ValueError):
         model.compile(optimizer, loss,
@@ -2305,9 +2339,10 @@ class TestTrainingWithDataTensors(keras_parameterized.TestCase):
                     sample_weight_mode=None,
                     target_tensors={'dense_1': y, 'dropout': y1})
       _ = model.train_on_batch([input_a_np, input_b_np],
-                               [output_a_np, output_b_np],
+                               [output_a_np, output_b_np], {
-                               {y: np.random.random((10, 4)),
+                                   'dense_1': np.random.random((10,)),
-                                y1: np.random.random((10, 3))})
+                                   'dropout': np.random.random((10,))
+                               })
 
       # test with custom TF placeholder as target
       pl_target_a = keras.backend.array_ops.placeholder('float32',

tensorflow/python/keras/engine/training_utils.py

@@ -380,7 +380,8 @@ def standardize_sample_or_class_weights(x_weight, output_names, weight_type):
                        'You should provide one `' + weight_type + '`'
                        'array per model output.')
     return x_weight
-  if isinstance(x_weight, dict):
+  if isinstance(x_weight, collections.Mapping):
+    generic_utils.check_for_unexpected_keys(weight_type, x_weight, output_names)
     x_weights = []
     for name in output_names:
       x_weights.append(x_weight.get(name))