TensorFlow: Initial commit of TensorFlow library.

TensorFlow is an open source software library for numerical computation
using data flow graphs.

Base CL: 107276108

1 files changed, 175 insertions, 0 deletions

diff --git a/tensorflow/g3doc/tutorials/mnist/input_data.py b/tensorflow/g3doc/tutorials/mnist/input_data.py
new file mode 100644
index 0000000000..88892027ff
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+++ b/tensorflow/g3doc/tutorials/mnist/input_data.py
@@ -0,0 +1,175 @@
+"""Functions for downloading and reading MNIST data."""
+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