Update app.py

app.py

@@ -39,6 +39,7 @@ class GeneticAlgorithm:
         fitness = []
         for i, net in enumerate(self.population):
             net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+            net.build(input_shape=(None, 10))  # Compile the model before training
             net.fit(X_train, y_train, epochs=10, verbose=0)
             loss, accuracy = net.evaluate(X_test, y_test, verbose=0)
             fitness.append(accuracy)
@@ -99,34 +100,38 @@ 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))  # Compile the model before training
             net.fit(X_train, y_train, epochs=10, verbose=0)
-            loss, acc = net.evaluate(X_test, y_test, verbose=0)
-            accuracy.append(acc)
-        final_accuracy.append(np.mean(accuracy))
-    st.write(f"Final accuracy: {np.mean(final_accuracy)}")
+            loss, acc = net.evaluate(X
 
-    # Trade populations between tasks
-    for i in range(num_tasks):
-        for j in range(i+1, num_tasks):
-            ga1 = gas[i]
-            ga2 = gas[j]
-            population1 = ga1.population
-            population2 = ga2.population
-            num_trade = int(0.1 * population_size)
-            trade1 = random.sample(population1, num_trade)
-            trade2 = random.sample(population2, num_trade)
-            ga1.population = population1 + trade2
-            ga2.population = population2 + trade1
+        loss, acc = net.evaluate(X_test, y_test, verbose=0)
+        accuracy.append(acc)
+    final_accuracy.append(np.mean(accuracy))
+st.write(f"Final accuracy: {np.mean(final_accuracy)}")
 
-    # Evaluate the final population after trading
-    final_accuracy_after_trade = []
-    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.fit(X_train, y_train, epochs=10, verbose=0)
-            loss, acc = net.evaluate(X_test, y_test, verbose=0)
-            accuracy.append(acc)
-        final_accuracy_after_trade.append(np.mean(accuracy))
-    st.write(f"Final accuracy after trading: {np.mean(final_accuracy_after_trade)}")
+# Trade populations between tasks
+for i in range(num_tasks):
+    for j in range(i+1, num_tasks):
+        ga1 = gas[i]
+        ga2 = gas[j]
+        population1 = ga1.population
+        population2 = ga2.population
+        num_trade = int(0.1 * population_size)
+        trade1 = random.sample(population1, num_trade)
+        trade2 = random.sample(population2, num_trade)
+        ga1.population = population1 + trade2
+        ga2.population = population2 + trade1
+
+# Evaluate the final population after trading
+final_accuracy_after_trade = []
+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))  # Compile the model before training
+        net.fit(X_train, y_train, epochs=10, verbose=0)
+        loss, acc = net.evaluate(X_test, y_test, verbose=0)
+        accuracy.append(acc)
+    final_accuracy_after_trade.append(np.mean(accuracy))
+st.write(f"Final accuracy after trading: {np.mean(final_accuracy_after_trade)}")