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