23-07-12

xor iris 수치 교정
파티클의 분포 조정 가능하게 수정
random 시드 추출

pso/optimizer.py

@@ -41,6 +41,8 @@ class Optimizer:
         mutation_swarm: float = 0,
         np_seed: int = None,
         tf_seed: int = None,
+        particle_min: float = -5,
+        particle_max: float = 5,
     ):
         """
         particle swarm optimization
@@ -63,6 +65,8 @@ class Optimizer:
         if tf_seed is not None:
             tf.random.set_seed(tf_seed)
 
+        self.random_state = np.random.get_state()
+
         self.model = model  # 모델 구조
         self.loss = loss  # 손실함수
         self.n_particles = n_particles  # 파티클 개수
@@ -82,6 +86,7 @@ class Optimizer:
         self.renewal = "acc"
         self.Dispersion = False
         self.day = datetime.now().strftime("%m-%d-%H-%M")
+        self.empirical_balance = False
 
         negative_count = 0
 
@@ -89,7 +94,7 @@ class Optimizer:
             m = keras.models.model_from_json(model.to_json())
             init_weights = m.get_weights()
             w_, sh_, len_ = self._encode(init_weights)
-            w_ = np.random.uniform(-3, 3, len(w_))
+            w_ = np.random.uniform(particle_min, particle_max, len(w_))
             m.set_weights(self._decode(w_, sh_, len_))
             m.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
             self.particles[i] = Particle(
@@ -254,6 +259,7 @@ class Optimizer:
             elif renewal == "both":
                 if local_score[1] > self.g_best_score[0]:
                     self.g_best_score[0] = local_score[1]
+                    self.g_best_score[1] = local_score[0]
                     self.g_best = p.get_best_weights()
                     self.g_best_ = p.get_best_weights()
 
@@ -274,7 +280,6 @@ class Optimizer:
                     else:
                         f.write("\n")
 
-                    f.close()
             del local_score
             gc.collect()
 
@@ -424,7 +429,6 @@ class Optimizer:
                                 f.write(", ")
                             else:
                                 f.write("\n")
-                        f.close()
 
                 if check_point is not None:
                     if epoch % check_point == 0:
@@ -498,6 +502,11 @@ class Optimizer:
             "Dispersion": self.Dispersion,
             "negative_swarm": self.negative_swarm,
             "mutation_swarm": self.mutation_swarm,
+            "random_state_0": self.random_state[0],
+            "random_state_1": self.random_state[1].tolist(),
+            "random_state_2": self.random_state[2],
+            "random_state_3": self.random_state[3],
+            "random_state_4": self.random_state[4],
             "renewal": self.renewal,
         }
 
@@ -507,8 +516,6 @@ class Optimizer:
         ) as f:
             json.dump(json_save, f, indent=4)
 
-        f.close()
-
     def _check_point_save(self, save_path: str = f"./result/check_point"):
         """
         중간 저장