23-10-20

조기 수렴 시 파티클 리셋 적용
모델의 초기화 수정 => 랜덤값은 문제가 많음
미니배치 초기화 시 자동 shuffle 적용
negative 파티클 특정 수치마다 초기화

mnist.py

@@ -33,34 +33,18 @@ def get_data():
     return x_train, y_train, x_test, y_test
 
 
-def get_data_test():
-    (x_train, y_train), (x_test, y_test) = mnist.load_data()
-    x_test = x_test / 255.0
-    x_test = x_test.reshape((10000, 28, 28, 1))
-
-    y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
-
-    x_train, x_test = tf.convert_to_tensor(
-        x_train), tf.convert_to_tensor(x_test)
-    y_train, y_test = tf.convert_to_tensor(
-        y_train), tf.convert_to_tensor(y_test)
-
-    print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
-
-    return x_test, y_test
-
-
 def make_model():
     model = Sequential()
     model.add(
         Conv2D(32, kernel_size=(5, 5), activation="sigmoid",
                input_shape=(28, 28, 1))
     )
-    model.add(MaxPooling2D(pool_size=(3, 3)))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
     model.add(Conv2D(64, kernel_size=(3, 3), activation="sigmoid"))
     model.add(MaxPooling2D(pool_size=(2, 2)))
-    model.add(Dropout(0.25))
     model.add(Flatten())
+    model.add(Dropout(0.25))
+    model.add(Dense(256, activation="sigmoid"))
     model.add(Dense(128, activation="sigmoid"))
     model.add(Dense(10, activation="softmax"))
 
@@ -107,15 +91,19 @@ loss = [
 pso_mnist = optimizer(
     model,
     loss="mean_squared_error",
-    n_particles=600,
+    n_particles=900,
     c0=0.2,
     c1=0.4,
     w_min=0.3,
     w_max=0.5,
     negative_swarm=0.05,
     mutation_swarm=0.3,
-    particle_min=-4,
-    particle_max=4,
+    particle_min=-0.3,
+    particle_max=0.3,
+    early_stopping=True,
+    early_stopping_patience=10,
+    early_stopping_monitor="loss",
+    early_stopping_min_delta=0.0005,
 )
 
 best_score = pso_mnist.fit(
@@ -130,7 +118,7 @@ best_score = pso_mnist.fit(
     check_point=25,
     empirical_balance=False,
     dispersion=False,
-    batch_size=32,
+    batch_size=1024,
 )
 
 print("Done!")