23-06-03

tensorflow gpu 의 메모리 용량 제한을 추가
readme에 분류 문제별 해결 현황 추가

pso/particle.py

@@ -12,7 +12,7 @@ class Particle:
         self.loss = loss
         init_weights = self.model.get_weights()
         i_w_, s_, l_ = self._encode(init_weights)
-        i_w_ = np.random.rand(len(i_w_)) / 5 - 0.10
+        i_w_ = np.random.rand(len(i_w_)) / 2 - 0.25
         self.velocities = self._decode(i_w_, s_, l_)
         self.negative = negative
         self.best_score = 0
@@ -40,7 +40,7 @@ class Particle:
             lenght.append(len(w_))
             # w_gpu = cp.append(w_gpu, w_)
             w_gpu = np.append(w_gpu, w_)
-        gc.collect()
+
         return w_gpu, shape, lenght
 
     """
@@ -62,7 +62,7 @@ class Particle:
         del start, end, w_
         del shape, lenght
         del weight
-        gc.collect()
+
         return weights
 
     def get_score(self, x, y, renewal: str = "acc"):
@@ -77,7 +77,7 @@ class Particle:
             if score[0] < self.best_score:
                 self.best_score = score[0]
                 self.best_weights = self.model.get_weights()
-        gc.collect()
+
         return score
 
     def _update_velocity(self, local_rate, global_rate, w, g_best):
@@ -105,7 +105,6 @@ class Particle:
         del encode_p, p_sh, p_len
         del encode_g, g_sh, g_len
         del r0, r1
-        gc.collect()
 
     def _update_velocity_w(self, local_rate, global_rate, w, w_p, w_g, g_best):
         encode_w, w_sh, w_len = self._encode(weights=self.model.get_weights())
@@ -132,7 +131,6 @@ class Particle:
         del encode_p, p_sh, p_len
         del encode_g, g_sh, g_len
         del r0, r1
-        gc.collect()
 
     def _update_weights(self):
         encode_w, w_sh, w_len = self._encode(weights=self.model.get_weights())
@@ -141,12 +139,10 @@ class Particle:
         self.model.set_weights(self._decode(new_w, w_sh, w_len))
         del encode_w, w_sh, w_len
         del encode_v, v_sh, v_len
-        gc.collect()
 
     def f(self, x, y, weights):
         self.model.set_weights(weights)
         score = self.model.evaluate(x, y, verbose=0)[1]
-        gc.collect()
         if score > 0:
             return 1 / (1 + score)
         else:
@@ -155,7 +151,6 @@ class Particle:
     def step(self, x, y, local_rate, global_rate, w, g_best, renewal: str = "acc"):
         self._update_velocity(local_rate, global_rate, w, g_best)
         self._update_weights()
-        gc.collect()
         return self.get_score(x, y, renewal)
 
     def step_w(
@@ -163,7 +158,6 @@ class Particle:
     ):
         self._update_velocity_w(local_rate, global_rate, w, w_p, w_g, g_best)
         self._update_weights()
-        gc.collect()
         return self.get_score(x, y, renewal)
 
     def get_best_score(self):