在学习神经网络是时，读入mnist数据集时总是出现错误，测试了各种·方法都不能有效的解决
代码如下
import sys,os
sys.path.append(os.pardir)
from dataset.mnist import load_mnist
( x_train, t_train), (x_test, t_test) = load_mnist(normalize=False, flatten=True)
print(x_train.shape)
print(t_train.shape)
print(x_test.shape)
print(t_test.shape)
import numpy as np
from PIL import Image
def img_show(img):
pil_img=Image.fromarray(np.uint8(img))
pil_img.show()
( x_train, t_train), (x_test, t_test) = load_mnist(normalize=False, flatten=True)
img=x_train[0]
lable=t_train[0]
print(lable)
print(img.shape) #(784,)
img=img.reshape(28,28) #把图片的形状变为原来的尺寸
print(img.shape) #28*28
img_show()错误是这样
Downloading t10k-labels-idx1-ubyte.gz ...
Traceback (most recent call last):
File "E:\pycharm\ch3\minst_show.py", line 4, in <module>
( x_train, t_train), (x_test, t_test) = load_mnist(normalize=False, flatten=True)
File "E:\pycharm\dataset\mnist.py", line 106, in load_mnist
init_mnist()
File "E:\pycharm\dataset\mnist.py", line 75, in init_mnist
download_mnist()
File "E:\pycharm\dataset\mnist.py", line 42, in download_mnist
_download(v)
File "E:\pycharm\dataset\mnist.py", line 37, in _download
urllib.request.urlretrieve(url_base + file_name, file_path)
File "C:\Python\Python39\lib\urllib\request.py", line 239, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "C:\Python\Python39\lib\urllib\request.py", line 214, in urlopen
return opener.open(url, data, timeout)
File "C:\Python\Python39\lib\urllib\request.py", line 523, in open
response = meth(req, response)
File "C:\Python\Python39\lib\urllib\request.py", line 632, in http_response
response = self.parent.error(
File "C:\Python\Python39\lib\urllib\request.py", line 561, in error
return self._call_chain(*args)
File "C:\Python\Python39\lib\urllib\request.py", line 494, in _call_chain
result = func(*args)
File "C:\Python\Python39\lib\urllib\request.py", line 641, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 503: Service Unavailable
HTTP Error 503: Service Unavailable，访问的地址错了。
这是因为在http://yann.lecun.com/exdb/mnist/的最后一个文件暂时不提供下载服务，尝试从其他地方下载到本地调用，也可用替代方法：
from torchvision.datasets import MNIST
MNIST(".", download=True)from torchvision import datasets
datasets.MNIST.resources = [
('https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz', 'f68b3c2dcbeaaa9fbdd348bbdeb94873'),
('https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz', 'd53e105ee54ea40749a09fcbcd1e9432'),
('https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz', '9fb629c4189551a2d022fa330f9573f3'),
('https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz', 'ec29112dd5afa0611ce80d1b7f02629c')
]示例，从keras 可正常导入：
from keras.datasets import mnist
from matplotlib import pyplot
#loading
(train_X, train_y), (test_X, test_y) = mnist.load_data()
#shape of dataset
print('X_train: ' + str(train_X.shape))
print('Y_train: ' + str(train_y.shape))
print('X_test: ' + str(test_X.shape))
print('Y_test: ' + str(test_y.shape))
#plotting
for i in range(9):
pyplot.subplot(330 + 1 + i)
pyplot.imshow(train_X[i], cmap=pyplot.get_cmap('gray'))
pyplot.show()
# 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/'
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 + " ... ")
urllib.request.urlretrieve(url_base + file_name, file_path)
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):
"""读入MNIST数据集
Parameters
----------
normalize : 将图像的像素值正规化为0.0~1.0
one_hot_label :
one_hot_label为True的情况下，标签作为one-hot数组返回
one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组
flatten : 是否将图像展开为一维数组
Returns
-------
(训练图像, 训练标签), (测试图像, 测试标签)
"""
if not os.path.exists(save_file):
init_mnist()
with open(save_file, 'rb') as f:
dataset = pickle.load(f)
if normalize:
for key in ('train_img', 'test_img'):
dataset[key] = dataset[key].astype(np.float32)
dataset[key] /= 255.0
if one_hot_label:
dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
dataset['test_label'] = _change_one_hot_label(dataset['test_label'])
if not flatten:
for key in ('train_img', 'test_img'):
dataset[key] = dataset[key].reshape(-1, 1, 28, 28)
return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label'])
if __name__ == '__main__':
init_mnist()
这个是mnist代码也是书上给的源代码
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