Update import statement in mobilevit tutorial for TF>=2.13v (#1546)

* Update import statement in mobilevit tutorial for TF>=2.13v

The mobile vit tutorial will break due to import statement which was changed from Tf2.13V onwards.This works fine upto Tf2.12 versions.

Hence I changed the import statement to make it work for Tf2.13 and higher versions. Also added a note regarding this to make users aware of how to change the import for TF<=2.12 versions.

* Update mobilevit.py

* Correcting formatting errors in new code of mobilevit.py

Correcting formatting errors in new code of mobilevit.py

* Update import statement in mobilevit tutorial for TF>=2.13v

The mobile vit tutorial will break due to import statement which was changed from Tf2.13V onwards.This works fine upto Tf2.12 versions.

Hence I changed the import statement to make it work for Tf2.13 and higher versions. Also added a note regarding this to make users aware of how to change the import for TF<=2.12 versions.

* Update mobilevit.ipynb

Added Note in new line.

* Update import statement in mobilevit.md tutorial for TF>=2.13v

The mobile vit tutorial will break due to import statement which was changed from Tf2.13V onwards.This works fine upto Tf2.12 versions.

Hence I changed the import statement to make it work for Tf2.13 and higher versions. Also added a note regarding this to make users aware of how to change the import for TF<=2.12 versions.

Author: SuryanarayanaY

examples/vision/ipynb/mobilevit.ipynb

@@ -34,7 +34,9 @@
     "MobileViT as a general-purpose mobile-friendly backbone for different image recognition\n",
     "tasks. Their findings suggest that, performance-wise, MobileViT is better than other\n",
     "models with the same or higher complexity ([MobileNetV3](https://arxiv.org/abs/1905.02244),\n",
-    "for example), while being efficient on mobile devices."
+    "for example), while being efficient on mobile devices.\n",
+    "\n",
+    "Note: This example should be run with Tensorflow 2.13 and higher."
    ]
   },
   {
@@ -56,7 +58,9 @@
    "source": [
     "import tensorflow as tf\n",
     "\n",
-    "from keras.applications import imagenet_utils\n",
+    "from keras.src.applications import imagenet_utils\n",
+    "# For versions <TF2.13 change the above import to:\n",
+    "# from keras.applications import imagenet_utils\n",
     "from tensorflow.keras import layers\n",
     "from tensorflow import keras\n",
     "\n",
@@ -540,4 +544,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

examples/vision/md/mobilevit.md

@@ -27,14 +27,19 @@ tasks. Their findings suggest that, performance-wise, MobileViT is better than o
 models with the same or higher complexity ([MobileNetV3](https://arxiv.org/abs/1905.02244),
 for example), while being efficient on mobile devices.
 
+Note: This example should be run with Tensorflow 2.13 and higher.
+
 ---
 ## Imports
 
 
 ```python
 import tensorflow as tf
 
-from keras.applications import imagenet_utils
+from keras.src.applications import imagenet_utils
+# For versions <TF2.13 change the above import to:
+# from keras.applications import imagenet_utils
+
 from tensorflow.keras import layers
 from tensorflow import keras
 
@@ -840,4 +845,4 @@ open("mobilevit_xxs.tflite", "wb").write(tflite_model)
 To learn more about different quantization recipes available in TFLite and running
 inference with TFLite models, check out
 [this official resource](https://www.tensorflow.org/lite/performance/post_training_quantization).
-You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/mobile-vit-xxs) and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/Flowers-Classification-MobileViT).
+You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/mobile-vit-xxs) and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/Flowers-Classification-MobileViT).

examples/vision/mobilevit.py

@@ -22,6 +22,8 @@
 tasks. Their findings suggest that, performance-wise, MobileViT is better than other
 models with the same or higher complexity ([MobileNetV3](https://arxiv.org/abs/1905.02244),
 for example), while being efficient on mobile devices.
+
+Note: This example should be run with Tensorflow 2.13 and higher.
 """
 
 """
@@ -30,7 +32,10 @@
 
 import tensorflow as tf
 
-from keras.applications import imagenet_utils
+from keras.src.applications import imagenet_utils
+
+# For versions <TF2.13 change the above import to:
+# from keras.applications import imagenet_utils
 from tensorflow.keras import layers
 from tensorflow import keras