mnist.py 下载 mnist 数据集

# coding: utf-8
try:
    import urllib.request
except ImportError:
    raise ImportError('You should use Python 3.x')
import os.path
import gzip
import pickle
import os
import numpy as np


#url_base = 'http://yann.lecun.com/exdb/mnist/'
url_base = 'https://ossci-datasets.s3.amazonaws.com/mnist/'  # mirror site

key_file = {
    'train_img':'train-images-idx3-ubyte.gz',
    'train_label':'train-labels-idx1-ubyte.gz',
    'test_img':'t10k-images-idx3-ubyte.gz',
    'test_label':'t10k-labels-idx1-ubyte.gz'
}

dataset_dir = os.path.dirname(os.path.abspath(__file__))
save_file = dataset_dir + "/mnist.pkl"

train_num = 60000
test_num = 10000
img_dim = (1, 28, 28)
img_size = 784


def _download(file_name):
    file_path = dataset_dir + "/" + file_name

    if os.path.exists(file_path):
        return

    print("Downloading " + file_name + " ... ")
    headers = {"User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:47.0) Gecko/20100101 Firefox/47.0"}
    request = urllib.request.Request(url_base+file_name, headers=headers)
    response = urllib.request.urlopen(request).read()
    with open(file_path, mode='wb') as f:
        f.write(response)
    print("Done")

def download_mnist():
    for v in key_file.values():
       _download(v)

def _load_label(file_name):
    file_path = dataset_dir + "/" + file_name

    print("Converting " + file_name + " to NumPy Array ...")
    with gzip.open(file_path, 'rb') as f:
            labels = np.frombuffer(f.read(), np.uint8, offset=8)
    print("Done")

    return labels

def _load_img(file_name):
    file_path = dataset_dir + "/" + file_name

    print("Converting " + file_name + " to NumPy Array ...")
    with gzip.open(file_path, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=16)
    data = data.reshape(-1, img_size)
    print("Done")

    return data

def _convert_numpy():
    dataset = {}
    dataset['train_img'] =  _load_img(key_file['train_img'])
    dataset['train_label'] = _load_label(key_file['train_label'])
    dataset['test_img'] = _load_img(key_file['test_img'])
    dataset['test_label'] = _load_label(key_file['test_label'])

    return dataset

def init_mnist():
    download_mnist()
    dataset = _convert_numpy()
    print("Creating pickle file ...")
    with open(save_file, 'wb') as f:
        pickle.dump(dataset, f, -1)
    print("Done!")

def _change_one_hot_label(X):
    T = np.zeros((X.size, 10))
    for idx, row in enumerate(T):
        row[X[idx]] = 1

    return T


def load_mnist(normalize=True, flatten=True, one_hot_label=False):
    """加载MNIST数据集

    参数:
    ----------
    normalize : 是否将图像像素值归一化到0.0~1.0之间
    one_hot_label :
        如果one_hot_label为True，则返回one-hot形式的标签
        one-hot形式的数组，例如[0,0,1,0,0,0,0,0,0,0]
    flatten : 是否将图像展平为一维数组

    返回:
    -------
    (训练图像, 训练标签), (测试图像, 测试标签)
    """
    # 如果缓存文件不存在，则初始化MNIST数据集
    if not os.path.exists(save_file):
        init_mnist()

    # 从pickle文件中加载数据集
    with open(save_file, 'rb') as f:
        dataset = pickle.load(f)

    # 对图像数据进行归一化处理
    if normalize:
        for key in ('train_img', 'test_img'):
            dataset[key] = dataset[key].astype(np.float32)
            dataset[key] /= 255.0

    # 将标签转换为one-hot形式
    if one_hot_label:
        dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
        dataset['test_label'] = _change_one_hot_label(dataset['test_label'])

    # 如果不展平图像数据，则重塑为(样本数, 通道数, 高度, 宽度)的形式
    if not flatten:
        for key in ('train_img', 'test_img'):
            dataset[key] = dataset[key].reshape(-1, 1, 28, 28)

    # 返回处理后的训练集和测试集
    return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label'])

if __name__ == '__main__':
    init_mnist()