Я скачал код, который реализует генетический алгоритм.Используется набор данных по умолчанию mnist.Я хочу изменить набор данных по умолчанию 'mnist', но в то же время я хочу знать структуру набора данных, чтобы я мог отформатировать свои данные так, как это делает mnist.Я также хотел узнать, например, если я уже отформатировал свои данные my_own_data_set, будет ли он корректным для подачи в вызов функции network.train(my_own_data_set)?Какой тип данных принимает функция train.network?
"""Entry point to evolving the neural network. Start here."""
import logging
from optimizer import Optimizer
from tqdm import tqdm
# Setup logging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%m/%d/%Y %I:%M:%S %p',
level=logging.DEBUG,
filename='log.txt'
)
def train_networks(networks, dataset):
"""Train each network.
Args:
networks (list): Current population of networks
dataset (str): Dataset to use for training/evaluating
"""
pbar = tqdm(total=len(networks))
for network in networks:
network.train(dataset)
pbar.update(1)
pbar.close()
def get_average_accuracy(networks):
"""Get the average accuracy for a group of networks.
Args:
networks (list): List of networks
Returns:
float: The average accuracy of a population of networks.
"""
total_accuracy = 0
for network in networks:
total_accuracy += network.accuracy
return total_accuracy / len(networks)
def generate(generations, population, nn_param_choices, dataset):
"""Generate a network with the genetic algorithm.
Args:
generations (int): Number of times to evole the population
population (int): Number of networks in each generation
nn_param_choices (dict): Parameter choices for networks
dataset (str): Dataset to use for training/evaluating
"""
optimizer = Optimizer(nn_param_choices)
networks = optimizer.create_population(population)
# Evolve the generation.
for i in range(generations):
logging.info("***Doing generation %d of %d***" %
(i + 1, generations))
# Train and get accuracy for networks.
train_networks(networks, dataset)
# Get the average accuracy for this generation.
average_accuracy = get_average_accuracy(networks)
# Print out the average accuracy each generation.
logging.info("Generation average: %.2f%%" % (average_accuracy * 100))
logging.info('-'*80)
# Evolve, except on the last iteration.
if i != generations - 1:
# Do the evolution.
networks = optimizer.evolve(networks)
# Sort our final population.
networks = sorted(networks, key=lambda x: x.accuracy, reverse=True)
# Print out the top 5 networks.
print_networks(networks[:5])
def print_networks(networks):
"""Print a list of networks.
Args:
networks (list): The population of networks
"""
logging.info('-'*80)
for network in networks:
network.print_network()
def main():
"""Evolve a network."""
generations = 10 # Number of times to evole the population.
population = 20 # Number of networks in each generation.
dataset = 'mnist'
nn_param_choices = {
'nb_neurons': [64, 128, 256, 512, 768, 1024],
'nb_layers': [1, 2, 3, 4],
'activation': ['relu', 'elu', 'tanh', 'sigmoid'],
'optimizer': ['rmsprop', 'adam', 'sgd', 'adagrad',
'adadelta', 'adamax', 'nadam'],
}
logging.info("***Evolving %d generations with population %d***" %
(generations, population))
generate(generations, population, nn_param_choices, dataset)
if __name__ == '__main__':
main()