Update Perceiver image classification example (keras-team#369)

* added perceiver image classifier

* removed tfa dependency and replaced tfa LAMB optimizer and Input names

* modify comment in tutorial

examples/keras_io/tensorflow/vision/perceiver_image_classification.py

@@ -45,7 +45,6 @@
 import tensorflow as tf
 import keras_core as keras
 from keras_core import layers
-import tensorflow_addons as tfa
 
 """
 ## Prepare the data
@@ -206,9 +205,9 @@ def create_cross_attention_module(
 ):
     inputs = {
         # Recieve the latent array as an input of shape [1, latent_dim, projection_dim].
-        "latent_array": layers.Input(shape=(latent_dim, projection_dim)),
+        "latent_array": layers.Input(shape=(latent_dim, projection_dim), name="latent_array"),
         # Recieve the data_array (encoded image) as an input of shape [batch_size, data_dim, projection_dim].
-        "data_array": layers.Input(shape=(data_dim, projection_dim)),
+        "data_array": layers.Input(shape=(data_dim, projection_dim), name="data_array"),
     }
 
     # Apply layer norm to the inputs
@@ -399,10 +398,10 @@ def call(self, inputs):
 
 
 def run_experiment(model):
-    # Create LAMB optimizer with weight decay.
-    optimizer = tfa.optimizers.LAMB(
+    # Create Adam optimizer with weight decay.
+    optimizer = keras.optimizers.Adam(
         learning_rate=learning_rate,
-        weight_decay_rate=weight_decay,
+        weight_decay=weight_decay,
     )
 
     # Compile the model.