Update app.py

app.py

@@ -35,45 +35,36 @@ 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)[-len(self.population)//2:]]
+        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)[-len(self.population)//2:]]
 
     def crossover(self):
         offspring = []
-    for _ in range(len(self.population)//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 = []
-        for w1, w2 in zip(parent1_weights, parent2_weights):
-            child_weights.append((w1 + w2) / 2)
-        
-        # Set the weights of the child network
-        child.set_weights(child_weights)
-        
-        offspring.append(child)
-    self.population += offspring
-        
-        # 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(len(self.population)//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 = []
+            for w1, w2 in zip(parent1_weights, parent2_weights):
+                child_weights.append((w1 + w2) / 2)
+            
+            # Set the weights of the child network
+            child.set_weights(child_weights)
+            
+            offspring.append(child)
+        self.population += offspring
 
     def mutation(self):
         for net in self.population: