Update app.py

app.py

@@ -45,43 +45,31 @@ class GeneticAlgorithm:
 
     def crossover(self):
         offspring = []
+        X = np.random.rand(1, 10)  # dummy input to build the layers
         for _ in range(self.population_size//2):
             parent1, parent2 = random.sample(self.population, 2)
             child = Net()
+            child(X)  # build the layers
+            parent1(X)  # build the layers
+            parent2(X)  # build the layers
             
             # Average the weights of the two parents
-            parent1_weights = parent1.fc1.get_weights()
-            parent2_weights = parent2.fc1.get_weights()
+            parent1_weights = parent1.get_weights()
+            parent2_weights = parent2.get_weights()
             child_weights = [(np.array(w1) + np.array(w2)) / 2 for w1, w2 in zip(parent1_weights, parent2_weights)]
-            child.fc1.set_weights(child_weights)
-            
-            parent1_weights = parent1.fc2.get_weights()
-            parent2_weights = parent2.fc2.get_weights()
-            child_weights = [(np.array(w1) + np.array(w2)) / 2 for w1, w2 in zip(parent1_weights, parent2_weights)]
-            child.fc2.set_weights(child_weights)
-            
-            parent1_weights = parent1.fc3.get_weights()
-            parent2_weights = parent2.fc3.get_weights()
-            child_weights = [(np.array(w1) + np.array(w2)) / 2 for w1, w2 in zip(parent1_weights, parent2_weights)]
-            child.fc3.set_weights(child_weights)
+            child.set_weights(child_weights)
             
             offspring.append(child)
         self.population += offspring
 
     def mutation(self):
+        X = np.random.rand(1, 10)  # dummy input to build the layers
         for net in self.population:
+            net(X)  # build the layers
             if random.random() < 0.1:
-                weights = net.fc1.get_weights()
-                new_weights = [np.array(w) + np.random.randn(*w.shape) * 0.1 for w in weights]
-                net.fc1.set_weights(new_weights)
-                
-                weights = net.fc2.get_weights()
-                new_weights = [np.array(w) + np.random.randn(*w.shape) * 0.1 for w in weights]
-                net.fc2.set_weights(new_weights)
-                
-                weights = net.fc3.get_weights()
+                weights = net.get_weights()
                 new_weights = [np.array(w) + np.random.randn(*w.shape) * 0.1 for w in weights]
-                net.fc3.set_weights(new_weights)
+                net.set_weights(new_weights)
 
 # Streamlit app
 st.title("Evolution of Sub-Models")