Error out when list input is provided with Normalization layer.

PiperOrigin-RevId: 379753763

keras/layers/preprocessing/normalization.py

@@ -14,14 +14,16 @@
 # ==============================================================================
 """Normalization preprocessing layer."""
 
-import tensorflow.compat.v2 as tf
-# pylint: disable=g-classes-have-attributes
-
-import numpy as np
 from keras import backend
 from keras.engine import base_preprocessing_layer
+import numpy as np
+import tensorflow.compat.v2 as tf
+
 from tensorflow.python.util.tf_export import keras_export
 
+# pylint: disable=g-classes-have-attributes
+
+
 
 @keras_export('keras.layers.experimental.preprocessing.Normalization')
 class Normalization(base_preprocessing_layer.PreprocessingLayer):
@@ -110,6 +112,12 @@ def __init__(self, axis=-1, mean=None, variance=None, **kwargs):
   def build(self, input_shape):
     super().build(input_shape)
 
+    if (isinstance(input_shape, (list, tuple)) and
+        all(isinstance(shape, tf.TensorShape) for shape in input_shape)):
+      raise ValueError('Normalization only accepts a single input. If you are '
+                       'passing a python list or tuple as a single input, '
+                       'please convert to a numpy array or `tf.Tensor`.')
+
     input_shape = tf.TensorShape(input_shape).as_list()
     if len(input_shape) == 1:
       input_shape = input_shape + [1]

keras/layers/preprocessing/normalization_test.py

@@ -164,6 +164,18 @@ def test_bad_axis_fail_build(self, axis):
     with self.assertRaisesRegex(ValueError, r"in the range"):
       layer.build([None, 2, 3])
 
+  def test_list_input(self):
+    with self.assertRaisesRegex(
+        ValueError, ("Normalization only accepts a single input. If you are "
+                     "passing a python list or tuple as a single input, "
+                     "please convert to a numpy array or `tf.Tensor`.")):
+      normalization.Normalization()([1, 2, 3])
+
+  def test_scalar_input(self):
+    with self.assertRaisesRegex(ValueError,
+                                "axis.*values must be in the range"):
+      normalization.Normalization()(1)
+
 
 @keras_parameterized.run_all_keras_modes(always_skip_v1=True)
 class NormalizationAdaptTest(keras_parameterized.TestCase,