Dataset：利用Python将已有mnist数据集通过移动像素上下左右的方法来扩大数据集为初始数据集的5倍—Jason niu

from __future__ import print_function

import cPickle
import gzip
import os.path
import random

import numpy as np

print("Expanding the MNIST training set")

if os.path.exists("../data/mnist_expanded.pkl.gz"): 
    print("The expanded training set already exists.  Exiting.")
else:
    f = gzip.open("../data/mnist.pkl.gz", 'rb')
    training_data, validation_data, test_data = cPickle.load(f)
    f.close()
    expanded_training_pairs = []
    j = 0
    for x, y in zip(training_data[0], training_data[1]):
        expanded_training_pairs.append((x, y))
        image = np.reshape(x, (-1, 28))
        j += 1
        if j % 1000 == 0: print("Expanding image number", j)

        for d, axis, index_position, index in [
                (1,  0, "first", 0),
                (-1, 0, "first", 27),
                (1,  1, "last",  0),
                (-1, 1, "last",  27)]:
            new_img = np.roll(image, d, axis)
            if index_position == "first": 
                new_img[index, :] = np.zeros(28)
            else: 
                new_img[:, index] = np.zeros(28)
            expanded_training_pairs.append((np.reshape(new_img, 784), y))
    random.shuffle(expanded_training_pairs)
    expanded_training_data = [list(d) for d in zip(*expanded_training_pairs)]
    print("Saving expanded data. This may take a few minutes.")
    f = gzip.open("../data/mnist_expanded.pkl.gz", "w")   
    cPickle.dump((expanded_training_data, validation_data, test_data), f)
    f.close()