Updates to 1st Udacity colab:

- re-make data extraction idempotent in case someone accidentally cancels the extraction.
- Python 3 compatibility (h/t @lzlarryli)
Change: 113079245

1 files changed, 21 insertions, 20 deletions

diff --git a/tensorflow/examples/udacity/1_notmnist.ipynb b/tensorflow/examples/udacity/1_notmnist.ipynb
index 6439c45314..e44622b48a 100644
--- a/tensorflow/examples/udacity/1_notmnist.ipynb
+++ b/tensorflow/examples/udacity/1_notmnist.ipynb
@@ -48,12 +48,13 @@
         "import matplotlib.pyplot as plt\n",
         "import numpy as np\n",
         "import os\n",
+        "import sys\n",
         "import tarfile\n",
-        "import urllib\n",
         "from IPython.display import display, Image\n",
         "from scipy import ndimage\n",
         "from sklearn.linear_model import LogisticRegression\n",
-        "import cPickle as pickle"
+        "from six.moves.urllib.request import urlretrieve\n",
+        "from six.moves import cPickle as pickle"
       ],
       "outputs": [],
       "execution_count": 0
@@ -185,17 +186,17 @@
         "def extract(filename):\n",
         "  tar = tarfile.open(filename)\n",
         "  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
-        "  if not os.path.isdir(root):\n",
-        "    print 'Extracting data for %s. This may take a while. Please wait.' % root\n",
-        "    sys.stdout.flush()\n",
-        "    tar.extractall()\n",
-        "    tar.close()\n",
-        "  data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root)) if d != '.DS_Store']\n",
+        "  print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
+        "  sys.stdout.flush()\n",
+        "  tar.extractall()\n",
+        "  tar.close()\n",
+        "  data_folders = [\n",
+        "    os.path.join(root, d) for d in sorted(os.listdir(root)) if d != '.DS_Store']\n",
         "  if len(data_folders) != num_classes:\n",
         "    raise Exception(\n",
         "      'Expected %d folders, one per class. Found %d instead.' % (\n",
         "        num_classes, len(data_folders)))\n",
-        "  print data_folders\n",
+        "  print(data_folders)\n",
         "  return data_folders\n",
         "  \n",
         "train_folders = extract(train_filename)\n",
@@ -289,7 +290,7 @@
         "  label_index = 0\n",
         "  image_index = 0\n",
         "  for folder in data_folders:\n",
-        "    print folder\n",
+        "    print(folder)\n",
         "    for image in os.listdir(folder):\n",
         "      if image_index >= max_num_images:\n",
         "        raise Exception('More images than expected: %d >= %d' % (\n",
@@ -304,7 +305,7 @@
         "        labels[image_index] = label_index\n",
         "        image_index += 1\n",
         "      except IOError as e:\n",
-        "        print 'Could not read:', image_file, ':', e, '- it\\'s ok, skipping.'\n",
+        "        print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
         "    label_index += 1\n",
         "  num_images = image_index\n",
         "  dataset = dataset[0:num_images, :, :]\n",
@@ -312,10 +313,10 @@
         "  if num_images < min_num_images:\n",
         "    raise Exception('Many fewer images than expected: %d < %d' % (\n",
         "        num_images, min_num_images))\n",
-        "  print 'Full dataset tensor:', dataset.shape\n",
-        "  print 'Mean:', np.mean(dataset)\n",
-        "  print 'Standard deviation:', np.std(dataset)\n",
-        "  print 'Labels:', labels.shape\n",
+        "  print('Full dataset tensor:', dataset.shape)\n",
+        "  print('Mean:', np.mean(dataset))\n",
+        "  print('Standard deviation:', np.std(dataset))\n",
+        "  print('Labels:', labels.shape)\n",
         "  return dataset, labels\n",
         "train_dataset, train_labels = load(train_folders, 450000, 550000)\n",
         "test_dataset, test_labels = load(test_folders, 18000, 20000)"
@@ -502,8 +503,8 @@
         "valid_labels = train_labels[:valid_size]\n",
         "train_dataset = train_dataset[valid_size:valid_size+train_size,:,:]\n",
         "train_labels = train_labels[valid_size:valid_size+train_size]\n",
-        "print 'Training', train_dataset.shape, train_labels.shape\n",
-        "print 'Validation', valid_dataset.shape, valid_labels.shape"
+        "print('Training', train_dataset.shape, train_labels.shape)\n",
+        "print('Validation', valid_dataset.shape, valid_labels.shape)"
       ],
       "outputs": [
         {
@@ -556,7 +557,7 @@
         "  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
         "  f.close()\n",
         "except Exception as e:\n",
-        "  print 'Unable to save data to', pickle_file, ':', e\n",
+        "  print('Unable to save data to', pickle_file, ':', e)\n",
         "  raise"
       ],
       "outputs": [],
@@ -599,7 +600,7 @@
       },
       "source": [
         "statinfo = os.stat(pickle_file)\n",
-        "print 'Compressed pickle size:', statinfo.st_size"
+        "print('Compressed pickle size:', statinfo.st_size)"
       ],
       "outputs": [
         {
@@ -653,4 +654,4 @@
       ]
     }
   ]
-}
+}
\ No newline at end of file