Question

我已经下载了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，以便可以将数据格式化为mnist的方式。例如，我还想知道，如果我已经格式化了数据my_own_data_set，则馈入函数调用network.train(my_own_data_set)是否有效？函数train.network接受哪种数据类型？

"""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()

Answer 1

每行是一个字符串数组，用引号引起来的785个字符串，用逗号分隔。训练集中约60,000行，测试集中约10,000行。

该行以...图片中其余行的标签开头。

“ 8”，“ 0”，“ 0”，“ 0”，“ 0”，“ 0”，“ 0”，“ 0”，“ 0”，“ 0”，“ 0”，“ 0” ，...“ 255” ...

因此，第一个字符串项是图像表示的数字，其余的是784个字符串，表示图像的0-255，这是一个28x28图像，其行首尾表示为一长串字符串。

每行

string [785]行

MNIST数据集结构

1 个答案: