Update app.py

app.py

@@ -36,28 +36,29 @@ class GeneticAlgorithm:
     def selection(self):
         X_train, X_test, y_train, y_test = generate_dataset(self.task_id)
         fitness = []
-    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:]]
+        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 = []
-    for _ in range(self.population_size//2):
-        parent1, parent2 = random.sample(self.population, 2)
-        child = Net()
-        child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-        
-        # Get the weights of the parent networks
-        parent1_weights = parent1.get_weights()
-        parent2_weights = parent2.get_weights()
-        # Average the weights of the two parents
-        child_weights = [(np.array(w1) + np.array(w2)) / 2 for w1, w2 in zip(parent1_weights, parent2_weights)]
-        child.set_weights(child_weights)
-        offspring.append(child)
-    self.population += offspring
+        for _ in range(self.population_size//2):
+            parent1, parent2 = random.sample(self.population, 2)
+            child = Net()
+            child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+            
+            # Get the weights of the parent networks
+            parent1_weights = parent1.get_weights()
+            parent2_weights = parent2.get_weights()
+            # Average the weights of the two parents
+            child_weights = [(np.array(w1) + np.array(w2)) / 2 for w1, w2 in zip(parent1_weights, parent2_weights)]
+            child.set_weights(child_weights)
+            offspring.append(child)
+        self.population += offspring
 
     def mutation(self):
         for net in self.population:
@@ -65,7 +66,6 @@ class GeneticAlgorithm:
                 weights = net.get_weights()
                 new_weights = [np.array(w) + np.random.randn(*w.shape) * 0.1 for w in weights]
                 net.set_weights(new_weights)
-
 # Streamlit app
 st.title("Evolution of Sub-Models")