Merge changes from github.

Change: 120185825

3 files changed, 6 insertions, 197 deletions

diff --git a/tensorflow/examples/tutorials/mnist/input_data.py b/tensorflow/examples/tutorials/mnist/input_data.py
index 07ed2c4f1c..f1081b1052 100644
--- a/tensorflow/examples/tutorials/mnist/input_data.py
+++ b/tensorflow/examples/tutorials/mnist/input_data.py
@@ -26,195 +26,4 @@ import numpy
 from six.moves import urllib
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
-
-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 tf.gfile.Exists(work_directory):
-    tf.gfile.MakeDirs(work_directory)
-  filepath = os.path.join(work_directory, filename)
-  if not tf.gfile.Exists(filepath):
-    with tempfile.NamedTemporaryFile() as tmpfile:
-      temp_file_name = tmpfile.name
-      urllib.request.urlretrieve(SOURCE_URL + filename, temp_file_name)
-      tf.gfile.Copy(temp_file_name, filepath)
-      with tf.gfile.GFile(filepath) as f:
-        size = f.Size()
-      print('Successfully downloaded', filename, 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 tf.gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) 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):
-  """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, num_classes=10):
-  """Extract the labels into a 1D uint8 numpy array [index]."""
-  print('Extracting', filename)
-  with tf.gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) 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, num_classes)
-    return labels
-
-
-class DataSet(object):
-
-  def __init__(self, images, labels, fake_data=False, one_hot=False,
-               dtype=tf.float32):
-    """Construct a DataSet.
-
-    one_hot arg is used only if fake_data is true.  `dtype` can be either
-    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
-    `[0, 1]`.
-    """
-    dtype = tf.as_dtype(dtype).base_dtype
-    if dtype not in (tf.uint8, tf.float32):
-      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
-                      dtype)
-    if fake_data:
-      self._num_examples = 10000
-      self.one_hot = one_hot
-    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])
-      if dtype == tf.float32:
-        # 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] * 784
-      if self.one_hot:
-        fake_label = [1] + [0] * 9
-      else:
-        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, dtype=tf.float32):
-  class DataSets(object):
-    pass
-  data_sets = DataSets()
-
-  if fake_data:
-    def fake():
-      return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
-    data_sets.train = fake()
-    data_sets.validation = fake()
-    data_sets.test = fake()
-    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, dtype=dtype)
-  data_sets.validation = DataSet(validation_images, validation_labels,
-                                 dtype=dtype)
-  data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
-
-  return data_sets
+from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax.py b/tensorflow/examples/tutorials/mnist/mnist_softmax.py
index 5aaca6bbac..81bce1a01b 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_softmax.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_softmax.py
@@ -43,8 +43,8 @@ y = tf.nn.softmax(tf.matmul(x, W) + b)
 
 # Define loss and optimizer
 y_ = tf.placeholder(tf.float32, [None, 10])
-cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
-train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
+cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
+train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 
 # Train
 tf.initialize_all_variables().run()
diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
index 6cbe6a0f7e..83bb5dd165 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -81,7 +81,7 @@ def build_dataset(words):
 data, count, dictionary, reverse_dictionary = build_dataset(words)
 del words  # Hint to reduce memory.
 print('Most common words (+UNK)', count[:5])
-print('Sample data', data[:10])
+print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
 
 data_index = 0
 
@@ -113,8 +113,8 @@ def generate_batch(batch_size, num_skips, skip_window):
 
 batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
 for i in range(8):
-  print(batch[i], '->', labels[i, 0])
-  print(reverse_dictionary[batch[i]], '->', reverse_dictionary[labels[i, 0]])
+  print(batch[i], reverse_dictionary[batch[i]],
+      '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
 
 # Step 4: Build and train a skip-gram model.