Update example "Train a Vision Transformer on small datasets" from keras 2 to 3 version

examples/vision/shiftvit.py

@@ -27,24 +27,15 @@
 In this example, we minimally implement the paper with close alignement to the author's
 [official implementation](https://github.com/microsoft/SPACH/blob/main/models/shiftvit.py).
 
-This example requires TensorFlow 2.9 or higher, as well as TensorFlow Addons, which can
-be installed using the following command:
-"""
-"""shell
-pip install -qq -U tensorflow-addons
-"""
-
-"""
 ## Setup and imports
 """
 
 import numpy as np
 import matplotlib.pyplot as plt
 
 import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-import tensorflow_addons as tfa
+import keras
+from keras import layers
 
 import pathlib
 import glob
@@ -280,7 +271,7 @@ def __init__(self, drop_path_prob, **kwargs):
     def call(self, x, training=False):
         if training:
             keep_prob = 1 - self.drop_path_prob
-            shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1)
+            shape = (tf.shape(x)[0],) + (1,) * (len(x.shape) - 1)
             random_tensor = keep_prob + tf.random.uniform(shape, 0, 1)
             random_tensor = tf.floor(random_tensor)
             return (x / keep_prob) * random_tensor
@@ -871,7 +862,7 @@ def get_config(self):
 )
 
 # Get the optimizer.
-optimizer = tfa.optimizers.AdamW(
+optimizer = keras.optimizers.AdamW(
     learning_rate=scheduled_lrs, weight_decay=config.weight_decay
 )
 
@@ -913,7 +904,7 @@ def get_config(self):
 
 It can be saved in TF SavedModel format only. In general, this is the recommended format for saving models as well.
 """
-model.save("ShiftViT")
+model.export("ShiftViT")
 
 """
 ## Model inference
@@ -932,12 +923,10 @@ def get_config(self):
 """
 **Load saved model**
 """
-# Custom objects are not included when the model is saved.
-# At loading time, these objects need to be passed for reconstruction of the model
-saved_model = tf.keras.models.load_model(
-    "ShiftViT",
-    custom_objects={"WarmUpCosine": WarmUpCosine, "AdamW": tfa.optimizers.AdamW},
-)
+saved_layer = keras.layers.TFSMLayer("ShiftViT")
+inputs = tf.keras.Input(shape=(config.input_shape))  # specify your input shape
+outputs = saved_layer(inputs)
+saved_model = tf.keras.Model(inputs, outputs)
 
 """
 **Utility functions for inference**

examples/vision/vit_small_ds.py

@@ -35,13 +35,7 @@
 example is inspired from
 [Image classification with Vision Transformer](https://keras.io/examples/vision/image_classification_with_vision_transformer/).
 
-_Note_: This example requires TensorFlow 2.6 or higher, as well as
-[TensorFlow Addons](https://www.tensorflow.org/addons), which can be
-installed using the following command:
-
-```python
-pip install -qq -U tensorflow-addons
-```
+_Note_: This example requires TensorFlow 3 or higher
 """
 """
 ## Setup
@@ -50,10 +44,9 @@
 import math
 import numpy as np
 import tensorflow as tf
-from tensorflow import keras
-import tensorflow_addons as tfa
+import keras
 import matplotlib.pyplot as plt
-from tensorflow.keras import layers
+from keras import layers
 
 # Setting seed for reproducibiltiy
 SEED = 42
@@ -355,7 +348,7 @@ def call(self, encoded_patches):
 """
 
 
-class MultiHeadAttentionLSA(tf.keras.layers.MultiHeadAttention):
+class MultiHeadAttentionLSA(keras.layers.MultiHeadAttention):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         # The trainable temperature term. The initial value is
@@ -499,7 +492,7 @@ def run_experiment(model):
         warmup_steps=warmup_steps,
     )
 
-    optimizer = tfa.optimizers.AdamW(
+    optimizer = keras.optimizers.AdamW(
         learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY
     )