23-05-24 | 2

pyplot 을 추가하여 loss 와 acc 가 학습할때 어떻게 변화하는지 적용

pso_tf.py

@@ -9,7 +9,7 @@ class PSO(object):
     Class implementing PSO algorithm
     """
 
-    def __init__(self, model: keras.models, x_train, y_train, loss_method=keras.losses.MeanSquaredError(), n_particles=5):
+    def __init__(self, model: keras.models, loss_method=keras.losses.MeanSquaredError(), n_particles=5):
         """
         Initialize the key variables.
 
@@ -31,10 +31,8 @@ class PSO(object):
             m = keras.models.model_from_json(self.model_structure)
             m.compile(loss=self.loss_method,
                       optimizer="adam", metrics=["accuracy"])
-            # m.fit(x_train, y_train, epochs=1, batch_size=32, verbose=0) # 결과가 너무 좋지 않아서 처음 초기화 할때 어느정도 위치를 수정
             self.particles_weights[_] = m.get_weights()
             # print(f"shape > {self.particles_weights[_][0]}")
-       
 
             # self.particles_weights.append(particle_node)
 
@@ -78,7 +76,8 @@ class PSO(object):
             n_particles)]  # 각 파티클의 최적값의 점수
         self.g_best_score = 0                  # 전역 최적값의 점수(초기화 - 무한대)
         self.g_history = []
-        self.all_cost_history = [[] for i in range(n_particles)]
+        self.loss_history = [[] for i in range(n_particles)]
+        self.acc_history = [[] for i in range(n_particles)]
         self.g_best_score_history = []
         self.history = []
 
@@ -224,12 +223,13 @@ class PSO(object):
                     if score[1] > self.g_best_score:
                         self.g_best_score = score[1]
                         self.g_best = self.particles_weights[i].copy()
-                        self.g_history.append(self.g_best)
+                        self.g_history.append(self.g_best.copy())
                         self.g_best_score_history.append(
                             self.g_best_score)
 
                 self.score = score
-                self.all_cost_history[i].append(score)
+                self.loss_history[i].append(score[0])
+                self.acc_history[i].append(score[1])
                 # if self.func(self.particles_weights[i]) < self.func(p_best):
                 # self.p_best[i] = self.particles_weights[i]
                 # if self.
@@ -240,7 +240,7 @@ class PSO(object):
                 # self.g_history.append(self.g_best)
                 # print(f"{i} particle score : {score[0]}")
             print(
-                f"loss avg : {self.score[0]/self.n_particles} | acc avg : {self.score[1]/self.n_particles} | best loss : {self.g_best_score}")
+                f"loss avg : {self.score[0]/self.n_particles} | acc avg : {self.score[1]/self.n_particles} | best score : {self.g_best_score}")
 
             # self.history.append(self.particles_weights.copy())
 
@@ -278,6 +278,6 @@ class PSO(object):
 
     def global_score_history(self):
         return self.g_best_score_history.copy()
-    
-    def all_cost(self):
-        return self.all_cost_history.copy()
+
+    def all_history(self):
+        return self.loss_history, self.acc_history.copy()