Update app.py

app.py

@@ -17,17 +17,14 @@ def generate_dataset(task_id):
 class Net(keras.Model):
     def __init__(self):
         super(Net, self).__init__()
-        self.fc1 = keras.layers.Dense(20, activation='relu', input_shape=(10,))
-        self.fc2 = keras.layers.Dense(10, activation='relu')
-        self.fc3 = keras.layers.Dense(2)
+        self.units = 20
+        self.units2 = 10
+        self.units3 = 2
 
     def build(self, input_shape):
-        self.fc1.build(input_shape)
-        output_shape = self.fc1.compute_output_shape(input_shape)
-        self.fc2.build(output_shape)
-        output_shape = self.fc2.compute_output_shape(output_shape)
-        self.fc3.build(output_shape)
-        self.built = True
+        self.fc1 = keras.layers.Dense(self.units, activation='relu', input_shape=(10,))
+        self.fc2 = keras.layers.Dense(self.units2, activation='relu')
+        self.fc3 = keras.layers.Dense(self.units3)
 
     def call(self, x):
         x = self.fc1(x)
@@ -55,23 +52,17 @@ class GeneticAlgorithm:
             self.population = [self.population[i] for i in np.argsort(fitness)[-self.population_size//2:]]
 
     def crossover(self):
-        offspring = []
-        X = np.random.rand(1, 10)
-        for _ in range(self.population_size//2):
-            parent1, parent2 = random.sample(self.population, 2)
-            child = Net()
-            child.build(input_shape=(None, 10))
-            parent1.build(input_shape=(None, 10))
-            parent2.build(input_shape=(None, 10))
-            
-            # Average the weights of the two parents
-            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
+    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)