Update app.py

app.py

@@ -46,27 +46,27 @@ class GeneticAlgorithm:
 
     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.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
+        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:
@@ -74,6 +74,7 @@ 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")