Update app.py

app.py

@@ -45,28 +45,28 @@ class GeneticAlgorithm:
             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)
-            
-            # Create the child instance with the correct weights
-            child = Net()
-            child.set_weights(child_weights)
-            child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-            
-            offspring.append(child)
-        self.population += offspring
+    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.fc1.set_weights(child_weights[:2])
+        child.fc2.set_weights(child_weights[2:4])
+        child.fc3.set_weights(child_weights[4:])
+        
+        offspring.append(child)
+    self.population += offspring
 
     def mutation(self):
         for net in self.population: