Fix 5D shape validation issues with concat layer

keras/src/layers/merging/concatenate.py

@@ -1,3 +1,5 @@
+import copy
+
 from keras.src import ops
 from keras.src.api_export import keras_export
 from keras.src.layers.merging.base_merge import Merge
@@ -50,15 +52,15 @@ def build(self, input_shape):
             return
 
         reduced_inputs_shapes = [list(shape) for shape in input_shape]
+        reduced_inputs_shapes_copy = copy.copy(reduced_inputs_shapes)
         shape_set = set()
-
-        for i in range(len(reduced_inputs_shapes)):
+        for i in range(len(reduced_inputs_shapes_copy)):
             # Convert self.axis to positive axis for each input
             # in case self.axis is a negative number
-            concat_axis = self.axis % len(reduced_inputs_shapes[i])
+            concat_axis = self.axis % len(reduced_inputs_shapes_copy[i])
             #  Skip batch axis.
             for axis, axis_value in enumerate(
-                reduced_inputs_shapes[i][1:], start=1
+                reduced_inputs_shapes_copy, start=1
             ):
                 # Remove squeezable axes (axes with value of 1)
                 # if not in the axis that will be used for concatenation

keras/src/layers/merging/merging_test.py

@@ -5,6 +5,7 @@
 from keras.src import backend
 from keras.src import layers
 from keras.src import models
+from keras.src import ops
 from keras.src import testing
 
 
@@ -339,6 +340,35 @@ def test_concatenate_with_mask(self):
         )
         self.assertAllClose(output._keras_mask, [[1, 1, 1, 1]])
 
+    def test_concatenate_errors(self):
+        # This should work
+        x1 = np.ones((1, 1, 1, 1, 5))
+        x2 = np.ones((1, 1, 1, 1, 4))
+        out = layers.Concatenate(axis=-1)([x1, x2])
+        self.assertEqual(ops.shape(out), (1, 1, 1, 1, 9))
+
+        # This won't
+        x1 = np.ones((1, 2, 1, 1, 5))
+        x2 = np.ones((1, 1, 1, 1, 4))
+        with self.assertRaisesRegex(
+            ValueError,
+            (
+                "requires inputs with matching shapes "
+                "except for the concatenation axis"
+            ),
+        ):
+            out = layers.Concatenate(axis=-1)([x1, x2])
+        x1 = np.ones((1, 2, 1, 2, 1))
+        x2 = np.ones((1, 1, 1, 3, 1))
+        with self.assertRaisesRegex(
+            ValueError,
+            (
+                "requires inputs with matching shapes "
+                "except for the concatenation axis"
+            ),
+        ):
+            out = layers.Concatenate(axis=1)([x1, x2])
+
     @parameterized.named_parameters(TEST_PARAMETERS)
     @pytest.mark.skipif(
         not backend.SUPPORTS_SPARSE_TENSORS,