Fixes a misleading error message when creating a functional tf.keras.Model().

The Keras functional API uses the keyword arguments `inputs` and `outputs`. Currently however, if someone misspells `outputs` (e.g. as `output`), they get the following error: `TypeError: ('Keyword argument not understood:', 'inputs')`. This is confusing as it suggests that there is a problem with `inputs`, not `output`.

This error might ideally be surfaced where it is detected (and then ignored in a `try` statement) in `Functional.__init__()`, however that would require a larger change.

Instead this error message is fixed by telling `Model.__init__()` to validate `"inputs'` and `'outputs'` with its `kwargs`. This is less ideal because these arguments are not supposed to be passed to `Model.__init__()`, but as the user thinks that they are calling simply calling `Model.__init__()` it should not cause them any confusion.

This fixes Keras [issue 13743](https://github.com/keras-team/keras/issues/13743).

PiperOrigin-RevId: 321668253
Change-Id: Ideff3cd9298f573b633a2e6e821fa77b1c862570

tensorflow/python/keras/engine/functional.py

@@ -107,10 +107,6 @@ class Functional(training_lib.Model):
 
   @trackable.no_automatic_dependency_tracking
   def __init__(self, inputs=None, outputs=None, name=None, trainable=True):
-    # generic_utils.validate_kwargs(
-    #     kwargs, {'name', 'trainable'},
-    #     'Functional models may only specify `name` and `trainable` keyword '
-    #     'arguments during initialization. Got an unexpected argument:')
     super(Functional, self).__init__(name=name, trainable=trainable)
     self._init_graph_network(inputs, outputs)
 

tensorflow/python/keras/engine/functional_test.py

@@ -2321,5 +2321,25 @@ class CacheCorrectnessTest(keras_parameterized.TestCase):
       # if training is not passed at runtime
       self.assertAllEqual(network(x), _call(x, None))
 
+
+class InputsOutputsErrorTest(keras_parameterized.TestCase):
+
+  @testing_utils.enable_v2_dtype_behavior
+  def test_input_error(self):
+    inputs = input_layer_lib.Input((10,))
+    outputs = layers.Dense(10)(inputs)
+    with self.assertRaisesRegex(
+        TypeError, "('Keyword argument not understood:', 'input')"):
+      models.Model(input=inputs, outputs=outputs)
+
+  @testing_utils.enable_v2_dtype_behavior
+  def test_output_error(self):
+    inputs = input_layer_lib.Input((10,))
+    outputs = layers.Dense(10)(inputs)
+    with self.assertRaisesRegex(
+        TypeError, "('Keyword argument not understood:', 'output')"):
+      models.Model(inputs=inputs, output=outputs)
+
+
 if __name__ == '__main__':
   test.main()

tensorflow/python/keras/engine/training.py

@@ -258,8 +258,10 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
     # The following are implemented as property functions:
     # self.trainable_weights
     # self.non_trainable_weights
-    generic_utils.validate_kwargs(kwargs, {'trainable', 'dtype', 'dynamic',
-                                           'name', 'autocast'})
+    # `inputs` / `outputs` will only appear in kwargs if either are misspelled.
+    generic_utils.validate_kwargs(kwargs, {
+        'trainable', 'dtype', 'dynamic', 'name', 'autocast', 'inputs', 'outputs'
+    })
     super(Model, self).__init__(**kwargs)
     # By default, Model is a subclass model, which is not in graph network.
     self._is_graph_network = False