Merge changes from github.

Change: 118532471

3 files changed, 11 insertions, 9 deletions

diff --git a/tensorflow/examples/udacity/1_notmnist.ipynb b/tensorflow/examples/udacity/1_notmnist.ipynb
index 9d864ccd37..2265445815 100644
--- a/tensorflow/examples/udacity/1_notmnist.ipynb
+++ b/tensorflow/examples/udacity/1_notmnist.ipynb
@@ -55,7 +55,10 @@
         "from scipy import ndimage\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from six.moves.urllib.request import urlretrieve\n",
-        "from six.moves import cPickle as pickle"
+        "from six.moves import cPickle as pickle\n",
+        "\n",
+        "# Config the matlotlib backend as plotting inline in IPython\n",
+        "%matplotlib inline"
       ],
       "outputs": [],
       "execution_count": 0
@@ -295,9 +298,8 @@
         "  image_files = os.listdir(folder)\n",
         "  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
         "                         dtype=np.float32)\n",
-        "  image_index = 0\n",
         "  print(folder)\n",
-        "  for image in os.listdir(folder):\n",
+        "  for image_index, image in enumerate(image_files):\n",
         "    image_file = os.path.join(folder, image)\n",
         "    try:\n",
         "      image_data = (ndimage.imread(image_file).astype(float) - \n",
@@ -305,11 +307,10 @@
         "      if image_data.shape != (image_size, image_size):\n",
         "        raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
         "      dataset[image_index, :, :] = image_data\n",
-        "      image_index += 1\n",
         "    except IOError as e:\n",
         "      print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
         "    \n",
-        "  num_images = image_index\n",
+        "  num_images = image_index + 1\n",
         "  dataset = dataset[0:num_images, :, :]\n",
         "  if num_images < min_num_images:\n",
         "    raise Exception('Many fewer images than expected: %d < %d' %\n",
diff --git a/tensorflow/examples/udacity/2_fullyconnected.ipynb b/tensorflow/examples/udacity/2_fullyconnected.ipynb
index c8815f631b..588b581a69 100644
--- a/tensorflow/examples/udacity/2_fullyconnected.ipynb
+++ b/tensorflow/examples/udacity/2_fullyconnected.ipynb
@@ -410,7 +410,7 @@
       "source": [
         "Let's now switch to stochastic gradient descent training instead, which is much faster.\n",
         "\n",
-        "The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `sesion.run()`."
+        "The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `session.run()`."
       ]
     },
     {
@@ -577,7 +577,7 @@
         "Problem\n",
         "-------\n",
         "\n",
-        "Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units (nn.relu()) and 1024 hidden nodes. This model should improve your validation / test accuracy.\n",
+        "Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units [nn.relu()](https://www.tensorflow.org/versions/r0.7/api_docs/python/nn.html#relu) and 1024 hidden nodes. This model should improve your validation / test accuracy.\n",
         "\n",
         "---"
       ]
diff --git a/tensorflow/examples/udacity/5_word2vec.ipynb b/tensorflow/examples/udacity/5_word2vec.ipynb
index 94ba37ee13..62dbec4e11 100644
--- a/tensorflow/examples/udacity/5_word2vec.ipynb
+++ b/tensorflow/examples/udacity/5_word2vec.ipynb
@@ -43,6 +43,7 @@
       "source": [
         "# These are all the modules we'll be using later. Make sure you can import them\n",
         "# before proceeding further.\n",
+        "%matplotlib inline\n",
         "from __future__ import print_function\n",
         "import collections\n",
         "import math\n",
@@ -521,12 +522,12 @@
         "    # note that this is expensive (~20% slowdown if computed every 500 steps)\n",
         "    if step % 10000 == 0:\n",
         "      sim = similarity.eval()\n",
-        "      for i in xrange(valid_size):\n",
+        "      for i in range(valid_size):\n",
         "        valid_word = reverse_dictionary[valid_examples[i]]\n",
         "        top_k = 8 # number of nearest neighbors\n",
         "        nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
         "        log = 'Nearest to %s:' % valid_word\n",
-        "        for k in xrange(top_k):\n",
+        "        for k in range(top_k):\n",
         "          close_word = reverse_dictionary[nearest[k]]\n",
         "          log = '%s %s,' % (log, close_word)\n",
         "        print(log)\n",