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from array import array as pyarray
from scipy.io import loadmat
from sklearn.decomposition import PCA
import gzip
import hashlib
import logging
import numpy as np
import os
import os.path
import struct
import sys
import wget
TRAIN_IMAGES_URL = "http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"
TRAIN_LABELS_URL = "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"
TEST_IMAGES_URL = "http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"
TEST_LABELS_URL = "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"
TRAIN_IMAGES_SHA256 = "440fcabf73cc546fa21475e81ea370265605f56be210a4024d2ca8f203523609"
TRAIN_LABELS_SHA256 = "3552534a0a558bbed6aed32b30c495cca23d567ec52cac8be1a0730e8010255c"
TEST_IMAGES_SHA256 = "8d422c7b0a1c1c79245a5bcf07fe86e33eeafee792b84584aec276f5a2dbc4e6"
TEST_LABELS_SHA256 = "f7ae60f92e00ec6debd23a6088c31dbd2371eca3ffa0defaefb259924204aec6"
TRAIN_SAMPLE_INDICES_FNAME = "mnist_train_sample.tbl"
TEST_SAMPLE_INDICES_FNAME = "mnist_test_sample.tbl"
FNAME_IMG = {
'train': 'train-images-idx3-ubyte.gz',
'test': 't10k-images-idx3-ubyte.gz'
}
FNAME_LBL = {
'train': 'train-labels-idx1-ubyte.gz',
'test': 't10k-labels-idx1-ubyte.gz'
}
def download_and_check(in_url, out_fname, sha256sum):
logging.info("Downloading '{}'".format(in_url))
wget.download(in_url, out_fname)
valid = False
with open(out_fname, "rb") as f:
valid = (hashlib.sha256(f.read()).hexdigest() == sha256sum)
return valid
def load_mnist(data="train", digits=np.arange(10)):
fname_img = FNAME_IMG[data]
fname_lbl = FNAME_LBL[data]
with gzip.open(fname_lbl, 'rb') as flbl:
magic_nr, size = struct.unpack(">II", flbl.read(8))
lbl = pyarray("b", flbl.read())
with gzip.open(fname_img, 'rb') as fimg:
magic_nr, size, rows, cols = struct.unpack(">IIII", fimg.read(16))
img = pyarray("B", fimg.read())
ind = [k for k in range(size) if lbl[k] in digits]
N = len(ind)
images = np.zeros((N, rows*cols), dtype=np.uint8)
labels = np.zeros((N, 1), dtype=np.int8)
for i in range(len(ind)):
m = ind[i]*rows*cols
n = (ind[i]+1)*rows*cols
images[i] = np.array(img[m:n])
labels[i] = lbl[ind[i]]
return images, labels
if __name__ == "__main__":
logging.basicConfig(filename="mnist_extract.log",
format="%(levelname)s:%(message)s",
level=logging.INFO)
# Get and check original data if needed
urls = [TRAIN_IMAGES_URL, TRAIN_LABELS_URL,
TEST_IMAGES_URL, TEST_LABELS_URL]
fnames = [FNAME_IMG['train'], FNAME_LBL['train'],
FNAME_IMG['test'], FNAME_LBL['test']]
sha256sums = [TRAIN_IMAGES_SHA256, TRAIN_LABELS_SHA256,
TEST_IMAGES_SHA256, TEST_LABELS_SHA256]
for url, fname, sha256sum in zip(urls, fnames, sha256sums):
if not os.path.exists(fname):
ok = download_and_check(url, fname, sha256sum)
if not ok:
logging.error("'{}' is corrupted; aborting".format(fname))
exit(1)
# We now have the original data
logging.info("Loading MNIST training data")
mnist_train = dict()
mnist_train['train_X'], mnist_train['train_labels'] = load_mnist("train")
train_size = mnist_train['train_X'].shape[0]
logging.info("Loading MNIST test data")
mnist_test = dict()
mnist_test['test_X'], mnist_test['test_labels'] = load_mnist("test")
test_size = mnist_test['test_X'].shape[0]
should_load_samples = False
if len(sys.argv) == 2 \
or (not os.path.exists(TRAIN_SAMPLE_INDICES_FNAME)) \
or (not os.path.exists(TEST_SAMPLE_INDICES_FNAME)):
sample_size = int(sys.argv[1])
if sample_size/2 > min(train_size, test_size):
print("sample size is too large")
should_load_samples = True
else:
logging.info("Generating {} samples".format(sample_size))
train_sample_indices = np.randint(0, train_size, sample_size / 2)
test_sample_indices = np.randint(0, test_size, sample_size / 2)
logging.info("Saving generated samples")
np.savetxt("mnist_train_sample.tbl", train_sample_indices, fmt="%u")
np.savetxt("mnist_test_sample.tbl", test_sample_indices, fmt="%u")
else:
should_load_samples = True
if should_load_samples:
logging.info("Loading samples")
train_sample_indices = np.loadtxt(TRAIN_SAMPLE_INDICES_FNAME, dtype=int)
test_sample_indices = np.loadtxt(TEST_SAMPLE_INDICES_FNAME, dtype=int)
sample_size = train_sample_indices.shape[0] \
+ test_sample_indices.shape[0]
logging.info("Extracting {} samples".format(sample_size))
train_samples = mnist_train['train_X'][train_sample_indices, :]
test_samples = mnist_test['test_X'][test_sample_indices, :]
mnist_sample = np.concatenate((train_samples, test_samples))
mnist_sample = PCA(n_components=512, whiten=True).fit_transform(mnist_sample)
train_labels = mnist_train['train_labels'][train_sample_indices]
test_labels = mnist_test['test_labels'][test_sample_indices]
mnist_sample_labels = np.concatenate((train_labels, test_labels))
logging.info("Saving extracted samples and their labels")
sample_fname = "mnist_{}.tbl".format(sample_size)
labels_fname = "mnist_{}.labels".format(sample_size)
np.savetxt(sample_fname, mnist_sample, fmt="%f")
np.savetxt(labels_fname, mnist_sample_labels, fmt="%u")
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