input_data.py

#!/usr/bin/env python

"""Functions for downloading and reading MNIST data."""
import gzip
import os
from six.moves.urllib.request import urlretrieve
import numpy
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, _ = urlretrieve(SOURCE_URL + filename, filepath)
 statinfo = os.stat(filepath)
 print('Succesfully 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)


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 DataSet(object):
 def __init__(self, images, labels, fake_data=False):
 if fake_data:
 self._num_examples = 10000
 else:
 assert images.shape[0] == labels.shape[0], (
 "images.shape: %s labels.shape: %s" % (images.shape,
 labels.shape))
 self._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)
 self._images = images
 self._labels = labels
 self._epochs_completed = 0
 self._index_in_epoch = 0

 @property
 def images(self):
 return self._images

 @property
 def labels(self):
 return self._labels

 @property
 def num_examples(self):
 return self._num_examples

 @property
 def epochs_completed(self):
 return self._epochs_completed

 def next_batch(self, batch_size, fake_data=False):
 """Return the next `batch_size` examples from this data set."""
 if fake_data:
 fake_image = [1.0 for _ in xrange(784)]
 fake_label = 0
 return [fake_image for _ in xrange(batch_size)], [
 fake_label for _ in xrange(batch_size)]
 start = self._index_in_epoch
 self._index_in_epoch += batch_size
 if self._index_in_epoch > self._num_examples:
 # Finished epoch
 self._epochs_completed += 1
 # Shuffle the data
 perm = numpy.arange(self._num_examples)
 numpy.random.shuffle(perm)
 self._images = self._images[perm]
 self._labels = self._labels[perm]
 # Start next epoch
 start = 0
 self._index_in_epoch = batch_size
 assert batch_size <= self._num_examples
 end = self._index_in_epoch
 return self._images[start:end], self._labels[start:end]


def read_data_sets(train_dir, fake_data=False, one_hot=False):
 class DataSets(object):
 pass
 data_sets = DataSets()
 if fake_data:
 data_sets.train = DataSet([], [], fake_data=True)
 data_sets.validation = DataSet([], [], fake_data=True)
 data_sets.test = DataSet([], [], fake_data=True)
 return data_sets
 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, train_dir)
 train_images = extract_images(local_file)
 local_file = maybe_download(TRAIN_LABELS, train_dir)
 train_labels = extract_labels(local_file, one_hot=one_hot)
 local_file = maybe_download(TEST_IMAGES, train_dir)
 test_images = extract_images(local_file)
 local_file = maybe_download(TEST_LABELS, 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:]
 data_sets.train = DataSet(train_images, train_labels)
 data_sets.validation = DataSet(validation_images, validation_labels)
 data_sets.test = DataSet(test_images, test_labels)
 return data_sets