Update app.py

app.py

@@ -43,8 +43,8 @@ class GeneticAlgorithm:
         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.build(input_shape=(None, 10))
+            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)
@@ -53,21 +53,19 @@ class GeneticAlgorithm:
 
     def crossover(self):
         offspring = []
-    for _ in range(self.population_size//2):
-        parent1, parent2 = random.sample(self.population, 2)
-        child = Net()
-        child.build(input_shape=(None, 10))
-        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.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.build(input_shape=(None, 10))
+            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.set_weights(child_weights)
+            offspring.append(child)
+        self.population += offspring
 
     def mutation(self):
-        X = np.random.rand(1, 10)
         for net in self.population:
-            net.build(input_shape=(None, 10))
             if random.random() < 0.1:
                 weights = net.get_weights()
                 new_weights = [np.array(w) + np.random.randn(*w.shape) * 0.1 for w in weights]
@@ -94,22 +92,19 @@ if st.button("Run evolution"):
             ga.mutation()
         st.write(f"Generation {generation+1} complete")
 
-        # Evaluate the final population
+    # Evaluate the final population
     if gas is not None:
         final_accuracy = []
-        for task_id, ga in enumerate(gas):
+                for task_id, ga in enumerate(gas):
             X_train, X_test, y_train, y_test = generate_dataset(task_id)
             accuracy = []
             for net in ga.population:
                 net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-                net.build(input_shape=(None, 10))
                 net.fit(X_train, y_train, epochs=10, verbose=0)
                 loss, acc = net.evaluate(X_test, y_test, verbose=0)
                 accuracy.append(acc)
             if len(accuracy) > 0:
                 final_accuracy.append(np.mean(accuracy))
-        if len(final_accuracy) > 0:
-            st.write(f"Final accuracy: {np.mean(final_accuracy)}")
 
     # Trade populations between tasks
     if gas is not None:
@@ -133,7 +128,6 @@ if st.button("Run evolution"):
             accuracy = []
             for net in ga.population:
                 net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-                net.build(input_shape=(None, 10))
                 net.fit(X_train, y_train, epochs=10, verbose=0)
                 loss, acc = net.evaluate(X_test, y_test, verbose=0)
                 accuracy.append(acc)