23-05-29

EBPSO 알고리즘 구현 - 선택지로 추가
random 으로 분산시키는 방법 구현 - 선택지로 추가
iris 기준 98퍼센트로 나오나 정확한 결과를 지켜봐야 할것으로 보임

psokeras/optimizer.py

@@ -0,0 +1,67 @@
+BIG_SCORE = 1.e6  # type: float
+
+import keras
+from psokeras.particle import Particle
+from .util import ProgressBar
+
+
+class Optimizer:
+    def __init__(self, model, loss,
+                 n=10,
+                 acceleration=0.1,
+                 local_rate=1.0,
+                 global_rate=1.0):
+
+        self.n_particles = n
+        self.structure = model.to_json()
+        self.particles = [None] * n
+        self.loss = loss
+        self.length = len(model.get_weights())
+
+        params = {'acc': acceleration, 'local_acc': local_rate, 'global_acc': global_rate}
+
+        for i in range(n-1):
+            m = keras.models.model_from_json(self.structure)
+            m.compile(loss=loss,optimizer='sgd')
+            self.particles[i] = Particle(m, params)
+
+        self.particles[n-1] = Particle(model, params)
+
+        self.global_best_weights = None
+        self.global_best_score = BIG_SCORE
+
+    def fit(self, x, y, steps=0, batch_size=32):
+        num_batches = x.shape[0] // batch_size
+
+        for i, p in enumerate(self.particles):
+            local_score = p.get_score(x, y)
+
+            if local_score < self.global_best_score:
+                self.global_best_score = local_score
+                self.global_best_weights = p.get_best_weights()
+
+        print("PSO -- Initial best score {:0.4f}".format(self.global_best_score))
+
+        bar = ProgressBar(steps, updates=20)
+
+        for i in range(steps):
+            for j in range(num_batches):
+                x_ = x[j*batch_size:(j+1)*batch_size,:]
+                y_ = y[j*batch_size:(j+1)*batch_size]
+
+                for p in self.particles:
+                    local_score = p.step(x_, y_, self.global_best_weights)
+
+                    if local_score < self.global_best_score:
+                        self.global_best_score = local_score
+                        self.global_best_weights = p.get_best_weights()
+
+            bar.update(i)
+
+        bar.done()
+
+    def get_best_model(self):
+        best_model = keras.models.model_from_json(self.structure)
+        best_model.set_weights(self.global_best_weights)
+        best_model.compile(loss=self.loss,optimizer='sgd')
+        return best_model