Fix RandomZoom issue.

keras/layers/preprocessing/random_zoom.py

@@ -227,8 +227,12 @@ def _get_zoom_matrix(self, zooms, image_height, image_width):
         #      [0 0 1]]
         # where the last entry is implicit.
         # zoom matrices are always float32.
-        x_offset = ((float(image_width) - 1.0) / 2.0) * (1.0 - zooms[:, 0:1])
-        y_offset = ((float(image_height) - 1.0) / 2.0) * (1.0 - zooms[:, 1:])
+        x_offset = ((self.backend.cast(image_width, "float32") - 1.0) / 2.0) * (
+            1.0 - zooms[:, 0:1]
+        )
+        y_offset = (
+            (self.backend.cast(image_height, "float32") - 1.0) / 2.0
+        ) * (1.0 - zooms[:, 1:])
         return self.backend.numpy.concatenate(
             [
                 zooms[:, 0:1],

keras/layers/preprocessing/random_zoom_test.py

@@ -4,6 +4,7 @@
 
 from keras import backend
 from keras import layers
+from keras import models
 from keras import testing
 
 
@@ -108,3 +109,14 @@ def test_tf_data_compatibility(self):
         for output in ds.take(1):
             output = output.numpy()
         self.assertAllClose(expected_output, output)
+
+    def test_dynamic_shape(self):
+        inputs = layers.Input((None, None, 3))
+        outputs = layers.RandomZoom(
+            height_factor=(0.5, 0.5),
+            width_factor=(0.8, 0.8),
+            interpolation="nearest",
+            fill_mode="constant",
+        )(inputs)
+        model = models.Model(inputs, outputs)
+        model.predict(np.random.random((1, 6, 6, 3)))