python读取mnist文件
从 http://yann.lecun.com/exdb/mnist/ 可以下载原始的文件。
train-images-idx3-ubyte.gz: training set images (9912422 bytes)
train-labels-idx1-ubyte.gz:
training set labels (28881 bytes)
t10k-images-idx3-ubyte.gz:
test set images (1648877 bytes)
t10k-labels-idx1-ubyte.gz:
test set labels (4542 bytes)
The training set contains 60000 examples, and the test set 10000 examples.
The first 5000 examples of the test set are taken from the original NIST training set. The last 5000 are taken from the original NIST test set. The first 5000 are cleaner and easier than the last 5000.
TRAINING SET LABEL FILE (train-labels-idx1-ubyte):
[offset] [type]
[value] [description]
0000 32 bit integer 0x00000801(2049)
magic number (MSB first)
0004 32 bit integer 60000
number of items
0008 unsigned byte ??
label
0009 unsigned byte ??
label
........
xxxx unsigned byte ??
label
The labels values are 0 to 9.
TRAINING SET IMAGE FILE (train-images-idx3-ubyte):
[offset] [type]
[value] [description]
0000 32 bit integer 0x00000803(2051)
magic number
0004 32 bit integer 60000
number of images
0008 32 bit integer 28
number of rows
0012 32 bit integer 28
number of columns
0016 unsigned byte ??
pixel
0017 unsigned byte ??
pixel
........
xxxx unsigned byte ??
pixel
Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).
TEST SET LABEL FILE (t10k-labels-idx1-ubyte):
[offset] [type]
[value] [description]
0000 32 bit integer 0x00000801(2049)
magic number (MSB first)
0004 32 bit integer 10000
number of items
0008 unsigned byte ??
label
0009 unsigned byte ??
label
........
xxxx unsigned byte ??
label
The labels values are 0 to 9.
TEST SET IMAGE FILE (t10k-images-idx3-ubyte):
[offset] [type]
[value] [description]
0000 32 bit integer 0x00000803(2051)
magic number
0004 32 bit integer 10000
number of images
0008 32 bit integer 28
number of rows
0012 32 bit integer 28
number of columns
0016 unsigned byte ??
pixel
0017 unsigned byte ??
pixel
........
xxxx unsigned byte ??
pixel
Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background
(white), 255 means foreground (black).
THE IDX FILE FORMAT
the IDX file format is a simple format for vectors and multidimensional matrices of various numerical types.
The basic format is
magic number
size in dimension 0
size in dimension 1
size in dimension 2
.....
size in dimension N
data
The magic number is an integer (MSB first). The first 2 bytes are always 0.
The third byte codes the type of the data:
0x08: unsigned byte
0x09: signed byte
0x0B: short (2 bytes)
0x0C: int (4 bytes)
0x0D: float (4 bytes)
0x0E: double (8 bytes)
The 4-th byte codes the number of dimensions of the vector/matrix: 1 for vectors, 2 for matrices....
The sizes in each dimension are 4-byte integers (MSB first, high endian, like in most non-Intel processors).
The data is stored like in a C array, i.e. the index in the last dimension changes the fastest.
python 读取 mnist 文件其实就是 python 怎么读取 binnary file。mnist 的结构如下，选取 train-images-idx3-ubyte
TRAINING SET IMAGE FILE (train-images-idx3-ubyte):
[offset] [type] [value] [description]
0000 32 bit integer 0x00000803(2051) magic number
0004 32 bit integer 60000 number of images
0008 32 bit integer 28 number of rows
0012 32 bit integer 28 number of columns
0016 unsigned byte ?? pixel
0017 unsigned byte ?? pixel
........
xxxx unsigned byte ?? pixel
也就是之前我们要读取4个 32 bit integer. 试过很多方法，觉得最方便的，至少对我来说还是使用 struct.unpack_from()
filename = 'train-images.idx3-ubyte'binfile = open(filename , 'rb')buf = binfile.read() |
先使用二进制方式把文件都读进来
index = 0magic, numImages , numRows , numColumns = struct.unpack_from('>IIII' , buf , index)index += struct.calcsize('>IIII') |
然后使用struc.unpack_from
'>IIII'是说使用大端法读取4个unsinged int32
然后读取一个图片测试是否读取成功
im = struct.unpack_from('>784B' ,buf, index)index += struct.calcsize('>784B')im = np.array(im)im = im.reshape(28,28)fig = plt.figure()plotwindow = fig.add_subplot(111)plt.imshow(im , cmap='gray')plt.show() |
'>784B'的意思就是用大端法读取784个unsigned byte
完整代码如下，读取其中第一个图像：
import numpy as np #python 3.7 import struct import matplotlib.pyplot as plt filename = 'train-images.idx3-ubyte' binfile = open(filename, 'rb') buf = binfile.read() index = 0 magic, numImages, numRows, numColumns = struct.unpack_from('>IIII', buf, index) index += struct.calcsize('>IIII') im = struct.unpack_from('>784B', buf, index) index += struct.calcsize('>784B') im = np.array(im) im = im.reshape(28, 28) fig = plt.figure() plotwindow = fig.add_subplot(111) plt.imshow(im, cmap='gray') plt.show() ### ### https://www.cnblogs.com/x1957/archive/2012/06/02/2531503.html ###
另外一个实例，读取全部图像：
## from https://www.jianshu.com/p/84f72791806f # encoding: utf-8 """ @author: monitor1379 @contact: yy4f5da2@hotmail.com @site: www.monitor1379.com @version: 1.0 @license: Apache Licence @file: mnist_decoder.py @time: 2016/8/16 20:03 对MNIST手写数字数据文件转换为bmp图片文件格式。 数据集下载地址为http://yann.lecun.com/exdb/mnist。 相关格式转换见官网以及代码注释。 ======================== 关于IDX文件格式的解析规则： ======================== THE IDX FILE FORMAT the IDX file format is a simple format for vectors and multidimensional matrices of various numerical types. The basic format is magic number size in dimension 0 size in dimension 1 size in dimension 2 ..... size in dimension N data The magic number is an integer (MSB first). The first 2 bytes are always 0. The third byte codes the type of the data: 0x08: unsigned byte 0x09: signed byte 0x0B: short (2 bytes) 0x0C: int (4 bytes) 0x0D: float (4 bytes) 0x0E: double (8 bytes) The 4-th byte codes the number of dimensions of the vector/matrix: 1 for vectors, 2 for matrices.... The sizes in each dimension are 4-byte integers (MSB first, high endian, like in most non-Intel processors). The data is stored like in a C array, i.e. the index in the last dimension changes the fastest. """ import numpy as np import struct import matplotlib.pyplot as plt # 训练集文件 train_images_idx3_ubyte_file = 'train-images.idx3-ubyte' # 训练集标签文件 train_labels_idx1_ubyte_file = 'train-labels.idx1-ubyte' # 测试集文件 test_images_idx3_ubyte_file = 't10k-images.idx3-ubyte' # 测试集标签文件 test_labels_idx1_ubyte_file = 't10k-labels.idx1-ubyte' def decode_idx3_ubyte(idx3_ubyte_file): """ 解析idx3文件的通用函数 :param idx3_ubyte_file: idx3文件路径 :return: 数据集 """ # 读取二进制数据 bin_data = open(idx3_ubyte_file, 'rb').read() # 解析文件头信息，依次为魔数、图片数量、每张图片高、每张图片宽 offset = 0 fmt_header = '>iiii' magic_number, num_images, num_rows, num_cols = struct.unpack_from(fmt_header, bin_data, offset) print('魔数:%d, 图片数量: %d张, 图片大小: %d*%d' % (magic_number, num_images, num_rows, num_cols)) # 解析数据集 image_size = num_rows * num_cols offset += struct.calcsize(fmt_header) fmt_image = '>' + str(image_size) + 'B' images = np.empty((num_images, num_rows, num_cols)) for i in range(num_images): if (i + 1) % 10000 == 0: print('已解析 %d' % (i + 1) + '张') images[i] = np.array(struct.unpack_from(fmt_image, bin_data, offset)).reshape((num_rows, num_cols)) offset += struct.calcsize(fmt_image) return images def decode_idx1_ubyte(idx1_ubyte_file): """ 解析idx1文件的通用函数 :param idx1_ubyte_file: idx1文件路径 :return: 数据集 """ # 读取二进制数据 bin_data = open(idx1_ubyte_file, 'rb').read() # 解析文件头信息，依次为魔数和标签数 offset = 0 fmt_header = '>ii' magic_number, num_images = struct.unpack_from(fmt_header, bin_data, offset) print('魔数:%d, 图片数量: %d张' % (magic_number, num_images)) # 解析数据集 offset += struct.calcsize(fmt_header) fmt_image = '>B' labels = np.empty(num_images) for i in range(num_images): if (i + 1) % 10000 == 0: print('已解析 %d' % (i + 1) + '张') labels[i] = struct.unpack_from(fmt_image, bin_data, offset)[0] offset += struct.calcsize(fmt_image) return labels def load_train_images(idx_ubyte_file=train_images_idx3_ubyte_file): """ TRAINING SET IMAGE FILE (train-images-idx3-ubyte): [offset] [type] [value] [description] 0000 32 bit integer 0x00000803(2051) magic number 0004 32 bit integer 60000 number of images 0008 32 bit integer 28 number of rows 0012 32 bit integer 28 number of columns 0016 unsigned byte ?? pixel 0017 unsigned byte ?? pixel ........ xxxx unsigned byte ?? pixel Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black). :param idx_ubyte_file: idx文件路径 :return: n*row*col维np.array对象，n为图片数量 """ return decode_idx3_ubyte(idx_ubyte_file) def load_train_labels(idx_ubyte_file=train_labels_idx1_ubyte_file): """ TRAINING SET LABEL FILE (train-labels-idx1-ubyte): [offset] [type] [value] [description] 0000 32 bit integer 0x00000801(2049) magic number (MSB first) 0004 32 bit integer 60000 number of items 0008 unsigned byte ?? label 0009 unsigned byte ?? label ........ xxxx unsigned byte ?? label The labels values are 0 to 9. :param idx_ubyte_file: idx文件路径 :return: n*1维np.array对象，n为图片数量 """ return decode_idx1_ubyte(idx_ubyte_file) def load_test_images(idx_ubyte_file=test_images_idx3_ubyte_file): """ TEST SET IMAGE FILE (t10k-images-idx3-ubyte): [offset] [type] [value] [description] 0000 32 bit integer 0x00000803(2051) magic number 0004 32 bit integer 10000 number of images 0008 32 bit integer 28 number of rows 0012 32 bit integer 28 number of columns 0016 unsigned byte ?? pixel 0017 unsigned byte ?? pixel ........ xxxx unsigned byte ?? pixel Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black). :param idx_ubyte_file: idx文件路径 :return: n*row*col维np.array对象，n为图片数量 """ return decode_idx3_ubyte(idx_ubyte_file) def load_test_labels(idx_ubyte_file=test_labels_idx1_ubyte_file): """ TEST SET LABEL FILE (t10k-labels-idx1-ubyte): [offset] [type] [value] [description] 0000 32 bit integer 0x00000801(2049) magic number (MSB first) 0004 32 bit integer 10000 number of items 0008 unsigned byte ?? label 0009 unsigned byte ?? label ........ xxxx unsigned byte ?? label The labels values are 0 to 9. :param idx_ubyte_file: idx文件路径 :return: n*1维np.array对象，n为图片数量 """ return decode_idx1_ubyte(idx_ubyte_file) def run(): train_images = load_train_images() train_labels = load_train_labels() # test_images = load_test_images() # test_labels = load_test_labels() # 查看前十个数据及其标签以读取是否正确 for i in range(3): print(train_labels[i]) plt.imshow(train_images[i], cmap='gray') plt.show() print('done') if __name__ == '__main__': run()
另外一个实例：
## https://www.e-learn.cn/content/wangluowenzhang/615391 import os import numpy as np import matplotlib.pyplot as plt def load_data(data_path): ''' 函数功能：导出MNIST数据 输入: data_path 传入数据所在路径（解压后的数据） 输出: train_data 输出data train_label 输出label ''' f_data = open(os.path.join(data_path, 'train-images.idx3-ubyte')) loaded_data = np.fromfile(file=f_data, dtype=np.uint8) # 前16个字符为说明符，需要跳过 train_data = loaded_data[16:].reshape((-1, 784)).astype(np.float) f_label = open(os.path.join(data_path, 'train-labels.idx1-ubyte')) loaded_label = np.fromfile(file=f_label, dtype=np.uint8) # 前8个字符为说明符，需要跳过 train_label = loaded_label[8:].reshape((-1)).astype(np.float) return train_data, train_label if __name__ == '__main__': train_data, train_label = load_data('./') ## path of files # 把下载好的minst数据集 放在xxxxxxx/minst文件夹里面；填入路径即可 print(np.shape(train_data)) # (60000, 784) print(np.shape(train_label)) # (60000,) for i in range(5): img = train_data[i].reshape(28, 28) # 变成二维图片 plt.imshow(img) plt.show() print(train_label[i]) # 输出了前五个图片 及其标签
From:
https://www.cnblogs.com/x1957/archive/2012/06/02/2531503.html
REF：
https://www.jianshu.com/p/84f72791806f
https://www.e-learn.cn/content/wangluowenzhang/615391
https://www.jianshu.com/p/84f72791806f
