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"""expand_rmnist.py
~~~~~~~~~~~~~~~~~~~
Take the RMNIST training images, and create expanded sets of training
images, by displacing each training image in a 3 x 3 region. Save the
resulting file to ../data/rmnist_expanded_n.pkl.gz.
"""
from __future__ import print_function
#### Libraries
import data_loader
# Standard library
import cPickle
import gzip
import os.path
import random
# Third-party libraries
import numpy as np
print("Expanding the RMNIST training sets")
def shift(image, d, axis):
if d == 0: return image
if d == 1: index = 0
if d == -1: index = 27
new_img = np.roll(image, d, axis)
if axis == 0:
if d == 2: new_img[1, :] = np.zeros(28)
if d >= 1: new_img[0, :] = np.zeros(28)
if d <= -1: new_img[27, :] = np.zeros(28)
if d == -2: new_img[26, :] = np.zeros(28)
if axis == 1:
if d == 2: new_img[:, 1] = np.zeros(28)
if d >= 1: new_img[:, 0] = np.zeros(28)
if d <= -1: new_img[:, 27] = np.zeros(28)
if d == -2: new_img[:, 26] = np.zeros(28)
return new_img
sizes = [1, 5, 10, 0]
for n in sizes:
print("\n\nExpanding RMNIST/{}".format(n))
td, vd, ts = data_loader.load_data(n)
expanded_training_pairs = []
j = 0 # counter
for x, y in zip(td[0], td[1]):
j += 1
if j % 10 == 0: print("Expanding image number", j)
image = np.reshape(x, (28, 28))
for dx in [1, 0, -1]:
for dy in [1, 0, -1]:
expanded_training_pairs.append(
(np.reshape(shift(shift(image, dx, 0), dy, 1), 784), y))
random.shuffle(expanded_training_pairs)
expanded_training_data = [list(d) for d in zip(*expanded_training_pairs)]
print("Saving expanded data.")
if n == 0: name = "data/mnist_expanded.pkl.gz"
if n > 0: name = "data/rmnist_expanded_{}.pkl.gz".format(n)
f = gzip.open(name, "w")
cPickle.dump((expanded_training_data, vd, ts), f)
f.close()