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MNISTData.py
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MNISTData.py
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# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import numpy
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def maybe_download(filename, work_directory):
"""Download the data from Yann's website, unless it's already here."""
if not os.path.exists(work_directory):
os.mkdir(work_directory)
filepath = os.path.join(work_directory, filename)
if not os.path.exists(filepath):
filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
statinfo = os.stat(filepath)
print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
return filepath
def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError('Invalid magic number %d in MNIST image file: %s' % (magic, filename))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
def dense_to_one_hot(labels_dense, num_classes=10):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def extract_labels(filename, one_hot=False):
"""Extract the labels into a 1D uint8 numpy array [index]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
raise ValueError('Invalid magic number %d in MNIST label file: %s' % (magic, filename))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
return dense_to_one_hot(labels)
return labels
class MNISTData(object):
def __init__(self, **kwargs):
self.train_dir = kwargs['train_dir'] if 'train_dir' in kwargs else None
one_hot = kwargs['one_hot'] if 'one_hot' in kwargs else False
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
VALIDATION_SIZE = 5000
local_file = maybe_download(TRAIN_IMAGES, self.train_dir)
train_images = extract_images(local_file)
local_file = maybe_download(TRAIN_LABELS, self.train_dir)
train_labels = extract_labels(local_file, one_hot=one_hot)
local_file = maybe_download(TEST_IMAGES, self.train_dir)
test_images = extract_images(local_file)
local_file = maybe_download(TEST_LABELS, self.train_dir)
test_labels = extract_labels(local_file, one_hot=one_hot)
validation_images = train_images[:VALIDATION_SIZE]
validation_labels = train_labels[:VALIDATION_SIZE]
train_images = train_images[VALIDATION_SIZE:]
train_labels = train_labels[VALIDATION_SIZE:]
self.train = self.setup(images=train_images, labels=train_labels)
self.validation = self.setup(images=validation_images, labels=validation_labels)
self.test = self.setup(images=test_images, labels=test_labels)
self.initialized = True
def setup(self, **kwargs):
assert ('images' in kwargs and 'labels' in kwargs), "images and labels arguments must be provided."
images = kwargs['images']
labels = kwargs['labels']
assert images.shape[0] == labels.shape[0], ('images.shape: %s labels.shape: %s' % (images.shape,labels.shape))
dic = {}
dic['num_examples'] = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
images = images.reshape(images.shape[0],images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
dic['images'] = images
dic['labels'] = labels
dic['epochs_completed'] = 0
dic['index_in_epoch'] = 0
return dic
def next_batch(self, n, **kwargs):
dataset = kwargs['set'] if 'set' in kwargs else self.train
"""Return the next `n` examples from this data set."""
start = dataset['index_in_epoch']
dataset['index_in_epoch'] += n
if dataset['index_in_epoch'] > dataset['num_examples']:
# Finished epoch
dataset['epochs_completed'] += 1
# Shuffle the data
perm = numpy.arange(dataset['num_examples'])
numpy.random.shuffle(perm)
dataset['images'] = dataset['images'][perm]
dataset['labels'] = dataset['labels'][perm]
# Start next epoch
start = 0
dataset['index_in_epoch'] = n
assert n <= dataset['num_examples']
end = dataset['index_in_epoch']
return dataset['images'][start:end], dataset['labels'][start:end]
def get_inputshape(self):
return [784]