我似乎无法在我的 Linux 中找到其他答案中提到的 Keras 数据集下载文件夹。
所以，我找到了一个有点笨拙但很容易解决这个问题的方法。原来有一种内置方法可以force download mnist 库中的文件。
- 只需转到您 pip 安装的 mnist 库副本。这可以在您的 python 虚拟环境中轻松找到。
venv/lib/python3.10/site-packages/mnist/__init__.py
- 现在，我们需要做的就是在这个文件中寻找
force=False并将它们设置为force=True
这是更新后的文件：
import os
import functools
import operator
import gzip
import struct
import array
import tempfile
try:
from urllib.request import urlretrieve
except ImportError:
from urllib import urlretrieve # py2
try:
from urllib.parse import urljoin
except ImportError:
from urlparse import urljoin
import numpy
__version__ = '0.2.2'
# `datasets_url` and `temporary_dir` can be set by the user using:
# >>> mnist.datasets_url = 'http://my.mnist.url'
# >>> mnist.temporary_dir = lambda: '/tmp/mnist'
datasets_url = 'http://yann.lecun.com/exdb/mnist/'
temporary_dir = tempfile.gettempdir
class IdxDecodeError(ValueError):
"""Raised when an invalid idx file is parsed."""
pass
def download_file(fname, target_dir=None, force=True):
"""Download fname from the datasets_url, and save it to target_dir,
unless the file already exists, and force is False.
Parameters
----------
fname : str
Name of the file to download
target_dir : str
Directory where to store the file
force : bool
Force downloading the file, if it already exists
Returns
-------
fname : str
Full path of the downloaded file
"""
target_dir = target_dir or temporary_dir()
target_fname = os.path.join(target_dir, fname)
if force or not os.path.isfile(target_fname):
url = urljoin(datasets_url, fname)
urlretrieve(url, target_fname)
return target_fname
def parse_idx(fd):
"""Parse an IDX file, and return it as a numpy array.
Parameters
----------
fd : file
File descriptor of the IDX file to parse
endian : str
Byte order of the IDX file. See [1] for available options
Returns
-------
data : numpy.ndarray
Numpy array with the dimensions and the data in the IDX file
1. https://docs.python.org/3/library/struct.html
#byte-order-size-and-alignment
"""
DATA_TYPES = {0x08: 'B', # unsigned byte
0x09: 'b', # signed byte
0x0b: 'h', # short (2 bytes)
0x0c: 'i', # int (4 bytes)
0x0d: 'f', # float (4 bytes)
0x0e: 'd'} # double (8 bytes)
header = fd.read(4)
if len(header) != 4:
raise IdxDecodeError('Invalid IDX file, '
'file empty or does not contain a full header.')
zeros, data_type, num_dimensions = struct.unpack('>HBB', header)
if zeros != 0:
raise IdxDecodeError('Invalid IDX file, '
'file must start with two zero bytes. '
'Found 0x%02x' % zeros)
try:
data_type = DATA_TYPES[data_type]
except KeyError:
raise IdxDecodeError('Unknown data type '
'0x%02x in IDX file' % data_type)
dimension_sizes = struct.unpack('>' + 'I' * num_dimensions,
fd.read(4 * num_dimensions))
data = array.array(data_type, fd.read())
data.byteswap() # looks like array.array reads data as little endian
expected_items = functools.reduce(operator.mul, dimension_sizes)
if len(data) != expected_items:
raise IdxDecodeError('IDX file has wrong number of items. '
'Expected: %d. Found: %d' % (expected_items,
len(data)))
return numpy.array(data).reshape(dimension_sizes)
def download_and_parse_mnist_file(fname, target_dir=None, force=True):
"""Download the IDX file named fname from the URL specified in dataset_url
and return it as a numpy array.
Parameters
----------
fname : str
File name to download and parse
target_dir : str
Directory where to store the file
force : bool
Force downloading the file, if it already exists
Returns
-------
data : numpy.ndarray
Numpy array with the dimensions and the data in the IDX file
"""
fname = download_file(fname, target_dir=target_dir, force=force)
fopen = gzip.open if os.path.splitext(fname)[1] == '.gz' else open
with fopen(fname, 'rb') as fd:
return parse_idx(fd)
def train_images():
"""Return train images from Yann LeCun MNIST database as a numpy array.
Download the file, if not already found in the temporary directory of
the system.
Returns
-------
train_images : numpy.ndarray
Numpy array with the images in the train MNIST database. The first
dimension indexes each sample, while the other two index rows and
columns of the image
"""
return download_and_parse_mnist_file('train-images-idx3-ubyte.gz')
def test_images():
"""Return test images from Yann LeCun MNIST database as a numpy array.
Download the file, if not already found in the temporary directory of
the system.
Returns
-------
test_images : numpy.ndarray
Numpy array with the images in the train MNIST database. The first
dimension indexes each sample, while the other two index rows and
columns of the image
"""
return download_and_parse_mnist_file('t10k-images-idx3-ubyte.gz')
def train_labels():
"""Return train labels from Yann LeCun MNIST database as a numpy array.
Download the file, if not already found in the temporary directory of
the system.
Returns
-------
train_labels : numpy.ndarray
Numpy array with the labels 0 to 9 in the train MNIST database.
"""
return download_and_parse_mnist_file('train-labels-idx1-ubyte.gz')
def test_labels():
"""Return test labels from Yann LeCun MNIST database as a numpy array.
Download the file, if not already found in the temporary directory of
the system.
Returns
-------
test_labels : numpy.ndarray
Numpy array with the labels 0 to 9 in the train MNIST database.
"""
return download_and_parse_mnist_file('t10k-labels-idx1-ubyte.gz')
- 为了提高效率，您可以将它们设置回
force=False，只要您不想再次下载它们（并且不要面对这个愚蠢的问题 xD）但是这些数据集无论如何都需要 1 秒才能下载，所以它应该永远不会成为大问题。