23-11-01

tensorboard 선택 시 자동으로 프로세스 실행
비어있는 포트를 자동으로 탐색하여 오픈
이전 최적해와 비교하여 관성치를 높게 주는 방법을 일시 폐기
digits 테스트 추가
tensorboard 자동 설치 추가

mnist.py

@@ -22,10 +22,8 @@ def get_data():
 
     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)
+    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_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
     print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
@@ -36,8 +34,7 @@ def get_data():
 def make_model():
     model = Sequential()
     model.add(
-        Conv2D(32, kernel_size=(5, 5), activation="relu",
-               input_shape=(28, 28, 1))
+        Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
     )
     model.add(MaxPooling2D(pool_size=(2, 2)))
     model.add(Dropout(0.5))
@@ -75,31 +72,31 @@ loss = [
 pso_mnist = optimizer(
     model,
     loss="categorical_crossentropy",
-    n_particles=100,
+    n_particles=500,
     c0=0.5,
-    c1=0.8,
-    w_min=0.6,
+    c1=0.3,
+    w_min=0.2,
     w_max=0.9,
     negative_swarm=0.0,
-    mutation_swarm=0.2,
+    mutation_swarm=0.1,
     convergence_reset=True,
     convergence_reset_patience=10,
-    convergence_reset_monitor="loss",
-    convergence_reset_min_delta=0.05,
+    convergence_reset_monitor="mse",
+    convergence_reset_min_delta=0.005,
 )
 
 best_score = pso_mnist.fit(
     x_train,
     y_train,
-    epochs=300,
+    epochs=500,
     save_info=True,
     log=2,
     log_name="mnist",
-    renewal="loss",
+    renewal="mse",
     check_point=25,
-    empirical_balance=True,
+    empirical_balance=False,
     dispersion=False,
-    batch_size=2048,
+    batch_size=10000,
     back_propagation=False,
     validate_data=(x_test, y_test),
 )