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from __future__ import print_function, division
import numpy as np
import copy
class ParticleSwarmOptimizedNN():
""" Particle Swarm Optimization of Neural Network.
n_individuals: int
The number of neural networks that are allowed in the population at a time.
model_builder: method
A method which returns a user specified NeuralNetwork instance.
inertia_weight: float [0,1)
cognitive_weight: float [0,1)
social_weight: float [0,1)
max_velocity: float
The maximum allowed value for the velocity.
Neural Network Training Using Particle Swarm Optimization
def __init__(self, population_size,
self.population_size = population_size
self.model_builder = model_builder
self.best_individual = None
# Parameters used to update velocity
self.cognitive_w = cognitive_weight
self.inertia_w = inertia_weight
self.social_w = social_weight
self.min_v = -max_velocity
self.max_v = max_velocity
def _build_model(self, id):
""" Returns a new individual """
model = self.model_builder(n_inputs=self.X.shape[1], n_outputs=self.y.shape[1]) = id = 0
model.highest_fitness = 0
model.accuracy = 0
# Set intial best as the current initialization
model.best_layers = copy.copy(model.layers)
# Set initial velocity to zero
model.velocity = []
for layer in model.layers:
velocity = {"W": 0, "w0": 0}
if hasattr(layer, 'W'):
velocity = {"W": np.zeros_like(layer.W), "w0": np.zeros_like(layer.w0)}
return model
def _initialize_population(self):
""" Initialization of the neural networks forming the population"""
self.population = []
for i in range(self.population_size):
model = self._build_model(id=i)
def _update_weights(self, individual):
""" Calculate the new velocity and update weights for each layer """
# Two random parameters used to update the velocity
r1 = np.random.uniform()
r2 = np.random.uniform()
for i, layer in enumerate(individual.layers):
if hasattr(layer, 'W'):
# Layer weights velocity
first_term_W = self.inertia_w * individual.velocity[i]["W"]
second_term_W = self.cognitive_w * r1 * (individual.best_layers[i].W - layer.W)
third_term_W = self.social_w * r2 * (self.best_individual.layers[i].W - layer.W)
new_velocity = first_term_W + second_term_W + third_term_W
individual.velocity[i]["W"] = np.clip(new_velocity, self.min_v, self.max_v)
# Bias weight velocity
first_term_w0 = self.inertia_w * individual.velocity[i]["w0"]
second_term_w0 = self.cognitive_w * r1 * (individual.best_layers[i].w0 - layer.w0)
third_term_w0 = self.social_w * r2 * (self.best_individual.layers[i].w0 - layer.w0)
new_velocity = first_term_w0 + second_term_w0 + third_term_w0
individual.velocity[i]["w0"] = np.clip(new_velocity, self.min_v, self.max_v)
# Update layer weights with velocity
individual.layers[i].W += individual.velocity[i]["W"]
individual.layers[i].w0 += individual.velocity[i]["w0"]
def _calculate_fitness(self, individual):
""" Evaluate the individual on the test set to get fitness scores """
loss, acc = individual.test_on_batch(self.X, self.y) = 1 / (loss + 1e-8)
individual.accuracy = acc
def evolve(self, X, y, n_generations):
""" Will evolve the population for n_generations based on dataset X and labels y"""
self.X, self.y = X, y
# The best individual of the population is initialized as population's first ind.
self.best_individual = copy.copy(self.population[0])
for epoch in range(n_generations):
for individual in self.population:
# Calculate new velocity and update the NN weights
# Calculate the fitness of the updated individual
# If the current fitness is higher than the individual's previous highest
# => update the individual's best layer setup
if > individual.highest_fitness:
individual.best_layers = copy.copy(individual.layers)
individual.highest_fitness =
# If the individual's fitness is higher than the highest recorded fitness for the
# whole population => update the best individual
if >
self.best_individual = copy.copy(individual)
print ("[%d Best Individual - ID: %d Fitness: %.5f, Accuracy: %.1f%%]" % (epoch,,,
return self.best_individual
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