FilesExpand file tree

 master

/

expand_mnist.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

60 lines (51 loc) · 1.94 KB

 master

/

expand_mnist.py

Copy path

Top

File metadata and controls

• Code
• Blame

60 lines (51 loc) · 1.94 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

"""expand_mnist.py

~~~~~~~~~~~~~~~~~~

Take the 50,000 MNIST training images, and create an expanded set of

250,000 images, by displacing each training image up, down, left and

right, by one pixel. Save the resulting file to

../data/mnist_expanded.pkl.gz.

Note that this program is memory intensive, and may not run on small

systems.

"""

from __future__ import print_function

#### Libraries

# Standard library

import cPickle

import gzip

import os.path

import random

# Third-party libraries

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 # counter

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)

# iterate over data telling us the details of how to

# do the displacement

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()