Update app.py

app.py

@@ -37,16 +37,14 @@ class GeneticAlgorithm:
     def selection(self):
         X_train, X_test, y_train, y_test = generate_dataset(self.task_id)
         fitness = []
-    for net in self.population:
-        net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-        net.fit(X_train, y_train, epochs=10, verbose=0)
-        loss, accuracy = net.evaluate(X_test, y_test, verbose=0)
-        fitness.append(accuracy)
-    if len(fitness) > 0:
-        self.population = [self.population[i] for i in np.argsort(fitness)[-self.population_size//2:]]
-    else:
-        print("Fitness list is empty. Cannot select individuals.")
-   
+        for i, net in enumerate(self.population):
+            net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+            net.fit(X_train, y_train, epochs=10, verbose=0)
+            loss, accuracy = net.evaluate(X_test, y_test, verbose=0)
+            fitness.append(accuracy)
+        if len(fitness) > 0:
+            self.population = [self.population[i] for i in np.argsort(fitness)[-self.population_size//2:]]
+
     def crossover(self):
         offspring = []
         X = np.random.rand(1, 10)  # dummy input to build the layers
@@ -100,12 +98,12 @@ if st.button("Run evolution"):
         X_train, X_test, y_train, y_test = generate_dataset(task_id)
         accuracy = []
         for net in ga.population:
-            net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+                        net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
             net.fit(X_train, y_train, epochs=10, verbose=0)
             loss, acc = net.evaluate(X_test, y_test, verbose=0)
             accuracy.append(acc)
         final_accuracy.append(np.mean(accuracy))
-        st.write(f"Final accuracy: {np.mean(final_accuracy)}")
+    st.write(f"Final accuracy: {np.mean(final_accuracy)}")
 
     # Trade populations between tasks
     for i in range(num_tasks):