/
mnist_dataset.py
62 lines (49 loc) · 2.4 KB
/
mnist_dataset.py
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## based on setup_mnist.py -- mnist data and model loading code
##
## Original Work Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
## Modifications Copyright (2019) IBM Corporation
import numpy as np
import os
import gzip
import urllib.request
def extract_data(filename, num_images):
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(num_images*28*28)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
data = (data / 255) - 0.5
data = data.reshape(num_images, 28, 28, 1)
return data
def extract_labels(filename, num_images):
with gzip.open(filename) as bytestream:
bytestream.read(8)
buf = bytestream.read(1 * num_images)
labels = np.frombuffer(buf, dtype=np.uint8)
return (np.arange(10) == labels[:, None]).astype(np.float32)
class MNISTDataset():
def __init__(self, custom_preprocessing=None, dirpath=None):
self._dirpath = dirpath
if not self._dirpath:
self._dirpath = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'..', 'data','mnist_data')
files = ["train-images-idx3-ubyte.gz",
"t10k-images-idx3-ubyte.gz",
"train-labels-idx1-ubyte.gz",
"t10k-labels-idx1-ubyte.gz"]
for name in files:
if not os.path.exists(self._dirpath + "/" + name):
print("retrieving file", name)
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/' + name, self._dirpath + "/" + name)
print("retrieved")
train_data = extract_data(self._dirpath + "/train-images-idx3-ubyte.gz", 60000)
train_labels = extract_labels(self._dirpath + "/train-labels-idx1-ubyte.gz", 60000)
self.test_data = extract_data(self._dirpath + "/t10k-images-idx3-ubyte.gz", 10000)
self.test_labels = extract_labels(self._dirpath + "/t10k-labels-idx1-ubyte.gz", 10000)
VALIDATION_SIZE = 5000
self.validation_data = train_data[:VALIDATION_SIZE, :, :, :]
self.validation_labels = train_labels[:VALIDATION_SIZE]
self.train_data = train_data[VALIDATION_SIZE:, :, :, :]
self.train_labels = train_labels[VALIDATION_SIZE:]