Added input_data to notebook folder

notebooks/3 - Neural Networks/input_data.py

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+"""Functions for downloading and reading MNIST data."""
+from __future__ import print_function
+import gzip
+import os
+import urllib
+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, _ = urllib.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