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EBPSO 알고리즘 구현 - 선택지로 추가 random 으로 분산시키는 방법 구현 - 선택지로 추가 iris 기준 98퍼센트로 나오나 정확한 결과를 지켜봐야 할것으로 보임
67 lines
2.3 KiB
Python
Executable File
67 lines
2.3 KiB
Python
Executable File
import random
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import numpy as np
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from psokeras.optimizer import BIG_SCORE
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class Particle:
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def __init__(self, model, params):
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self.model = model
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self.params = params
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self.init_weights = model.get_weights()
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self.velocities = [None] * len(self.init_weights)
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self.length = len(self.init_weights)
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for i, layer in enumerate(self.init_weights):
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self.velocities[i] = np.random.rand(*layer.shape) / 5 - 0.10
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# self.velocities[i] = np.zeros(layer.shape)
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self.best_weights = None
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self.best_score = BIG_SCORE
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def get_score(self, x, y, update=True):
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local_score = self.model.evaluate(x, y, verbose=0)
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if local_score < self.best_score and update:
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self.best_score = local_score
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self.best_weights = self.model.get_weights()
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return local_score
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def _update_velocities(self, global_best_weights, depth):
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new_velocities = [None] * len(self.init_weights)
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weights = self.model.get_weights()
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local_rand, global_rand = random.random(), random.random()
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for i, layer in enumerate(weights):
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if i >= depth:
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new_velocities[i] = self.velocities[i]
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continue
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new_v = self.params['acc'] * self.velocities[i]
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new_v = new_v + self.params['local_acc'] * local_rand * (self.best_weights[i] - layer)
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new_v = new_v + self.params['global_acc'] * global_rand * (global_best_weights[i] - layer)
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new_velocities[i] = new_v
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self.velocities = new_velocities
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def _update_weights(self, depth):
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old_weights = self.model.get_weights()
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new_weights = [None] * len(old_weights)
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for i, layer in enumerate(old_weights):
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if i>= depth:
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new_weights[i] = layer
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continue
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new_w = layer + self.velocities[i]
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new_weights[i] = new_w
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self.model.set_weights(new_weights)
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def step(self, x, y, global_best_weights,depth=None):
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if depth is None:
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depth = self.length
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self._update_velocities(global_best_weights, depth)
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self._update_weights(depth)
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return self.get_score(x, y)
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def get_best_weights(self):
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return self.best_weights
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