Update model.py

model.py

@@ -1,38 +1,30 @@
 import tensorflow as tf
-from tensorflow.keras import layers, models
+from tensorflow.keras.layers import Input, Embedding, LayerNormalization, MultiHeadAttention, Dense, Add, Dropout, Layer
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.losses import SparseCategoricalCrossentropy
+import numpy as np
 
-# Define your model architecture
-def create_model():
-    model = models.Sequential([
-        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
-        layers.MaxPooling2D((2, 2)),
-        layers.Conv2D(64, (3, 3), activation='relu'),
-        layers.MaxPooling2D((2, 2)),
-        layers.Conv2D(64, (3, 3), activation='relu'),
-        layers.Flatten(),
-        layers.Dense(64, activation='relu'),
-        layers.Dense(10, activation='softmax')
-    ])
-    return model
-
-# Load your data (example: MNIST dataset)
-mnist = tf.keras.datasets.mnist
-(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
-
-# Preprocess your data
-train_images = train_images.reshape((60000, 28, 28, 1)) / 255.0
-test_images = test_images.reshape((10000, 28, 28, 1)) / 255.0
-
-train_labels = tf.keras.utils.to_categorical(train_labels, 10)
-test_labels = tf.keras.utils.to_categorical(test_labels, 10)
-
-# Create an instance of your model
-model = create_model()
-
-# Compile the model
-model.compile(optimizer='adam',
-              loss='categorical_crossentropy',
-              metrics=['accuracy'])
-
-# Train the model
-model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_data=(test_images, test_labels))
+class VoidChatModel(tf.keras.Model):
+    def __init__(self, vocab_size, seq_len, num_layers=6, num_heads=8, emb_dim=512, mlp_dim=2048, dropout_rate=0.1):
+        super(VoidChatModel, self).__init__()
+        self.vocab_size = vocab_size
+        self.seq_len = seq_len
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.emb_dim = emb_dim
+        self.mlp_dim = mlp_dim
+        self.dropout_rate = dropout_rate
+        
+        # Embedding layer
+        self.embedding = Embedding(input_dim=vocab_size, output_dim=emb_dim)
+        
+        # Transformer layers
+        self.transformer_blocks = [TransformerBlock(num_heads, emb_dim, mlp_dim, dropout_rate) for _ in range(num_layers)]
+        
+        # Output layer
+        self.output_layer = Dense(vocab_size, activation='softmax')
+    
+    def call(self, input_ids, training=False):
+        # Embedding layer
+        x = self.embedding(input_ids)