Create model.py

model.py

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+# ----------------------------------------------------------
+# Enhanced Embedding Network
+# ----------------------------------------------------------
+import keras
+from keras_facenet import FaceNet
+from app import eucledian_distance
+
+def build_embedding_network(input_shape):
+    # Load the FaceNet model
+    facenet = FaceNet()
+    base_model = facenet.model
+
+    # Freeze all layers except the last 10 for fine-tuning
+    for layer in base_model.layers[:-10]:
+        layer.trainable = False
+
+    # Define input and pass it through the base model
+    inputs = keras.Input(input_shape)
+    x = base_model(inputs)  # Pass input through FaceNet
+
+    # Final Embedding Layer
+    x = layers.Dense(256, activation=None)(x)  # No activation before L2 norm
+    x = layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))(x)  # L2-normalized embeddings
+
+    return keras.Model(inputs, x)
+
+# ----------------------------------------------------------
+# Siamese Network
+# ----------------------------------------------------------
+def build_siamese():
+    
+    input_shape = (256, 256, 3)  # FaceNet expects 160x160 input
+    embedding_network = build_embedding_network(input_shape)
+
+# Define inputs for the Siamese network
+    input_1 = layers.Input(input_shape)
+    input_2 = layers.Input(input_shape)
+
+# Pass inputs through the embedding network
+    tower_1 = embedding_network(input_1)
+    tower_2 = embedding_network(input_2)
+
+# Compute Euclidean distance between embeddings
+    distance = layers.Lambda(euclidean_distance, name='distance_layer')([tower_1, tower_2])
+
+# Custom Contrastive Output Layer
+    output = layers.Dense(1, activation='sigmoid',
+                      kernel_regularizer=keras.regularizers.l2(1e-4))(distance)
+
+# Build the Siamese model
+    siamese = keras.Model(inputs=[input_1, input_2], outputs=output)
+    siamese.summary()