Fix Randomhue (#20652)

* Small fix in random hue

* use self.backend for seed

Author: IMvision12

keras/src/layers/preprocessing/image_preprocessing/random_hue.py

@@ -2,26 +2,6 @@
 from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import (  # noqa: E501
     BaseImagePreprocessingLayer,
 )
-from keras.src.random import SeedGenerator
-
-
-def transform_value_range(images, original_range, target_range):
-    if (
-        original_range[0] == target_range[0]
-        and original_range[1] == target_range[1]
-    ):
-        return images
-
-    original_min_value, original_max_value = original_range
-    target_min_value, target_max_value = target_range
-
-    # images in the [0, 1] scale
-    images = (images - original_min_value) / (
-        original_max_value - original_min_value
-    )
-
-    scale_factor = target_max_value - target_min_value
-    return (images * scale_factor) + target_min_value
 
 
 @keras_export("keras.layers.RandomHue")
@@ -70,7 +50,7 @@ def __init__(
         self._set_factor(factor)
         self.value_range = value_range
         self.seed = seed
-        self.generator = SeedGenerator(seed)
+        self.generator = self.backend.random.SeedGenerator(seed)
 
     def get_random_transformation(self, data, training=True, seed=None):
         if isinstance(data, dict):
@@ -107,7 +87,8 @@ def get_random_transformation(self, data, training=True, seed=None):
         return {"factor": invert * factor * 0.5}
 
     def transform_images(self, images, transformation=None, training=True):
-        images = transform_value_range(images, self.value_range, (0, 1))
+        images = self.backend.cast(images, self.compute_dtype)
+        images = self._transform_value_range(images, self.value_range, (0, 1))
         adjust_factors = transformation["factor"]
         adjust_factors = self.backend.cast(adjust_factors, images.dtype)
         adjust_factors = self.backend.numpy.expand_dims(adjust_factors, -1)
@@ -144,8 +125,8 @@ def transform_images(self, images, transformation=None, training=True):
         )
 
         images = self.backend.numpy.clip(images, 0, 1)
-        images = transform_value_range(images, (0, 1), self.value_range)
-
+        images = self._transform_value_range(images, (0, 1), self.value_range)
+        images = self.backend.cast(images, self.compute_dtype)
         return images
 
     def transform_labels(self, labels, transformation, training=True):