学习笔记-SNN用STDP法的MNIST数据集识别代码深入阅读

该代码Github地址：

https://github.com/peter-u-diehl/stdp-mnist/blob/master/Diehl%26Cook_spiking_MNIST.py

import numpy as np
import matplotlib.cm as cmap
import time
import os.path
import scipy 
import cPickle as pickle
import brian_no_units  #import it to deactivate unit checking --> This should NOT be done for testing/debugging 
import brian as b
from struct import unpack
from brian import *

# specify the location of the MNIST data
MNIST_data_path = ''

首先导入模块，定义数据集的路径变量。

def get_labeled_data(picklename, bTrain = True):
    """Read input-vector (image) and target class (label, 0-9) and return
       it as list of tuples.
    """
    if os.path.isfile('%s.pickle' % picklename):
        data = pickle.load(open('%s.pickle' % picklename))
    else:
        # Open the images with gzip in read binary mode
        if bTrain:
            images = open(MNIST_data_path + 'train-images.idx3-ubyte','rb')
            labels = open(MNIST_data_path + 'train-labels.idx1-ubyte','rb')
        else:
            images = open(MNIST_data_path + 't10k-images.idx3-ubyte','rb')
            labels = open(MNIST_data_path + 't10k-labels.idx1-ubyte','rb')
        # Get metadata for images
        images.read(4)  # skip the magic_number
        number_of_images = unpack('>I', images.read(4))[0]
        rows = unpack('>I', images.read(4))[0]
        cols = unpack('>I', images.read(4))[0]
        # Get metadata for labels
        labels.read(4)  # skip the magic_number
        N = unpack('>I', labels.read(4))[0]
    
        if number_of_images != N:
            raise Exception('number of labels did not match the number of images')
        # Get the data
        x = np.zeros((N, rows, cols), dtype=np.uint8)  # Initialize numpy array
        y = np.zeros((N, 1), dtype=np.uint8)  # Initialize numpy array
        for i in xrange(N):
            if i % 1000 == 0:
                print("i: %i" % i)
            x[i] = [[unpack('>B', images.read(1))[0] for unused_col in xrange(cols)]  for unused_row in xrange(rows) ]
            y[i] = unpack('>B', labels.read(1))[0]
            
        data = {'x': x, 'y': y, 'rows': rows, 'cols': cols}
        pickle.dump(data, open("%s.pickle" % picklename, "wb"))
    return data

获取含标签数据的函数。

函数功能：获得带标签的数据

输入：图片矩阵及其目标类（0-9的标签）、是否为训练数据

输出：元组列表

注：官方MNIS数据集有60000个训练集和10000个测试集，为IDX格式，IDX格式形式如下：

magic number
size in dimension 0
size in dimension 1
size in dimension 2
.....
size in dimension N
data

魔法数字是个整数，前两个字节总是0，第三个字节表示数据的类型：

0x08: unsigned byte
0x09: signed byte
0x0B: short (2 bytes)
0x0C: int (4 bytes)
0x0D: float (4 bytes)
0x0E: double (8 bytes)

第四个字节表示矩阵的维度。

接着便是每个维度的尺寸，用四字节的整数表示。

images.read(4)跳过了MNIST数据集的魔法数字的四个字节。

unpack是struct模块中的函数，用法是unpack(fmt, string)，代码中的'>'说明了改变对齐方式的方法，为大端对齐；'I'表示将C类型的unsigned int 转换为Python类型的integer。

获取到MNIST数据集中的数据后转换为numpy类型数组。

pickle提供了一个简单的持久化功能，可以将对象以文件的形式存放在磁盘上，dump方法：

pickle.dump(obj, file[, protocol])

def get_matrix_from_file(fileName):
    offset = len(ending) + 4
    if fileName[-4-offset] == 'X':
        n_src = n_input                
    else:
        if fileName[-3-offset]=='e':
            n_src = n_e
        else:
            n_src = n_i
    if fileName[-1-offset]=='e':
        n_tgt = n_e
    else:
        n_tgt = n_i
    readout = np.load(fileName)
    print readout.shape, fileName
    value_arr = np.zeros((n_src, n_tgt))
    if not readout.shape == (0,):
        value_arr[np.int32(readout[:,0]), np.int32(readout[:,1])] = readout[:,2]
    return value_arr