读取mnist数据集方法大全（train |
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读取mnist数据集方法大全（train
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文章目录
gzip包keras读取mnist数据集本地读取mnist数据集下载数据集解压读取方法一方法二
gzip包读取
读取bytes数据
注：import导入的包如果未安装使用pip安装
gzip包
如果仅仅是读取.gz文件使用gzip包即可。 例子：当前目录有一个input.gz文件，用以下代码来读取： import gzip with gzip.open('input.gz') as file: all_content = file.read()这样input.gz的文件都读取到了all_content里面 keras读取mnist数据集 from tensorflow import keras (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() 本地读取mnist数据集 下载数据集 数据集下载地址数据集界面：
解压读取
方法一
from mnist import MNIST
mndata = MNIST('samples')
images, labels = mndata.load_training()
# or
images, labels = mndata.load_testing()
index = random.randrange(0, len(images)) # choose an index ;-)
print(mndata.display(images[index]))
方法二
将.gz文件解压后读取 from mlxtend.data import loadlocal_mnist import platform if not platform.system() == 'Windows': X, y = loadlocal_mnist( images_path='train-images.idx3-ubyte', labels_path='train-labels.idx1-ubyte') else: X, y = loadlocal_mnist( images_path='train-images.idx3-ubyte', labels_path='train-labels.idx1-ubyte') print('Dimensions: %s x %s' % (X.shape[0], X.shape[1])) print('\n1st row', X[0]) gzip包读取 import gzip import numpy as np import matplotlib.pyplot as plt with gzip.open('train-images-idx3-ubyte.gz') as all_img: all_img = all_img.read() # print(all_img[:4]) # print((len(all_img)-16)/784) img1 = all_img[16:16+784] img = [] for i in range(28): for j in range(28): img.append(img1[28*i+j]) #print(img) img = np.array(img).reshape(28, 28) print(img.shape) plt.imshow(img) plt.show() 读取bytes数据参考stackoverflowConvert bytes to a string >>> b"abcde" b'abcde' # utf-8 is used here because it is a very common encoding, but you # need to use the encoding your data is actually in. >>> b"abcde".decode("utf-8") 'abcde' |
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