【发布时间】：2019-05-14 05:53:04
【问题描述】：
我下载了一个实现遗传算法的代码。它使用默认数据集mnist。我想更改默认数据集“mnist”，但同时我想知道数据集的结构，以便我可以按照mnist 的方式格式化我的数据。例如，我还想知道，如果我已经格式化了我的数据my_own_data_set，那么输入函数调用network.train(my_own_data_set) 是否有效？函数train.network接受什么数据类型？
"""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()
【问题讨论】：
-
“格式”是什么意思？
-
@cheersmate -> 我的意思是我想预处理我的数据，MNIST 在数据集中的结构方式。我想知道 MNIST 数据集的维度、它拥有的数据类型等等。
-
所以您可以在使用 MNIST 运行时首先检查数组形状、dtypes 等，然后为您自己的数据集添加一些检查？
-
@cheersmate 但数据集变量只是一个字符串，请参见上面的代码。数据集 = 'mnist'。我不知道 network.train(dataset) 如何提取数据进行训练。所以我无法检查它的尺寸。
-
你有源代码来了解内部发生了什么吗？
标签： python machine-learning neural-network genetic-algorithm mnist