隨著 Michael Nielsen 先生介紹『mnist_loader.py』程式，第一章也將步入尾聲。正好借此機緣，說說如何用 Python 『struct』程式庫

7.3. struct — Interpret strings as packed binary data

This module performs conversions between Python values and C structs represented as Python strings. This can be used in handling binary data stored in files or from network connections, among other sources. It uses Format Strings as compact descriptions of the layout of the C structs and the intended conversion to/from Python values.

……

7.3.2.1. Byte Order, Size, and Alignment

By default, C types are represented in the machine’s native format and byte order, and properly aligned by skipping pad bytes if necessary (according to the rules used by the C compiler).

Alternatively, the first character of the format string can be used to indicate the byte order, size and alignment of the packed data, according to the following table:

Character	Byte order	Size	Alignment
@	native	native	native
=	native	standard	none
<	little-endian	standard	none
>	big-endian	standard	none
!	network (= big-endian)	standard	none

If the first character is not one of these, '@' is assumed.

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7.3.2.2. Format Characters

Format characters have the following meaning; the conversion between C and Python values should be obvious given their types. The ‘Standard size’ column refers to the size of the packed value in bytes when using standard size; that is, when the format string starts with one of '<', '>', '!' or '='. When using native size, the size of the packed value is platform-dependent.

Format	C Type	Python type	Standard size	Notes
x	pad byte	no value
c	char	string of length 1	1
b	signed char	integer	1	(3)
B	unsigned char	integer	1	(3)
?	_Bool	bool	1	(1)
h	short	integer	2	(3)
H	unsigned short	integer	2	(3)
i	int	integer	4	(3)
I	unsigned int	integer	4	(3)
l	long	integer	4	(3)
L	unsigned long	integer	4	(3)
q	long long	integer	8	(2), (3)
Q	unsigned long long	integer	8	(2), (3)
f	float	float	4	(4)
d	double	float	8	(4)
s	char[]	string
p	char[]	string
P	void *	integer		(5), (3)

───

 

讀取原始『MNIST』之手寫阿拉伯數字資料庫︰

FILE FORMATS FOR THE MNIST DATABASE

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).

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希望短短的幾行互動程式足以盡其意也︰

>>> import struct
>>> import numpy as np

>>> with open("train-images.idx3-ubyte","rb") as imagefile:
...     magic, ni, nr, nc = struct.unpack(">IIII", imagefile.read(16))
...     images = np.fromfile(imagefile, dtype=np.uint8).reshape(60000,784)
... 
>>> len(images)
60000
>>> type(images[0])
<type 'numpy.ndarray'>
>>> len((images[0]))
784

>>> with open("train-labels.idx1-ubyte", "rb") as labelfile:
...     magic, ni =  struct.unpack(">II", labelfile.read(8))
...     labels = np.fromfile(labelfile, dtype=np.uint8)
... 
>>> len(labels)
60000
>>> labels[0]
5

>>> import matplotlib.pyplot as plt
>>> img = images[0].reshape(28,28)
>>> plt.imshow(img,cmap='Greys', interpolation='nearest')
<matplotlib.image.AxesImage object at 0x30f7b10>
>>> plt.show()
>>>