Merge changes from github.

PiperOrigin-RevId: 177526301

4 files changed, 20 insertions, 16 deletions

diff --git a/tensorflow/examples/how_tos/reading_data/convert_to_records.py b/tensorflow/examples/how_tos/reading_data/convert_to_records.py
index d14c1f7c86..c89e839563 100644
--- a/tensorflow/examples/how_tos/reading_data/convert_to_records.py
+++ b/tensorflow/examples/how_tos/reading_data/convert_to_records.py
@@ -52,17 +52,19 @@ def convert_to(data_set, name):
 
   filename = os.path.join(FLAGS.directory, name + '.tfrecords')
   print('Writing', filename)
-  writer = tf.python_io.TFRecordWriter(filename)
-  for index in range(num_examples):
-    image_raw = images[index].tostring()
-    example = tf.train.Example(features=tf.train.Features(feature={
-        'height': _int64_feature(rows),
-        'width': _int64_feature(cols),
-        'depth': _int64_feature(depth),
-        'label': _int64_feature(int(labels[index])),
-        'image_raw': _bytes_feature(image_raw)}))
-    writer.write(example.SerializeToString())
-  writer.close()
+  with tf.python_io.TFRecordWriter(filename) as writer:
+    for index in range(num_examples):
+      image_raw = images[index].tostring()
+      example = tf.train.Example(
+          features=tf.train.Features(
+              feature={
+                  'height': _int64_feature(rows),
+                  'width': _int64_feature(cols),
+                  'depth': _int64_feature(depth),
+                  'label': _int64_feature(int(labels[index])),
+                  'image_raw': _bytes_feature(image_raw)
+              }))
+      writer.write(example.SerializeToString())
 
 
 def main(unused_argv):
diff --git a/tensorflow/examples/speech_commands/input_data.py b/tensorflow/examples/speech_commands/input_data.py
index 6d75fbb92b..751652b330 100644
--- a/tensorflow/examples/speech_commands/input_data.py
+++ b/tensorflow/examples/speech_commands/input_data.py
@@ -240,7 +240,8 @@ class AudioProcessor(object):
     # Look through all the subfolders to find audio samples
     search_path = os.path.join(self.data_dir, '*', '*.wav')
     for wav_path in gfile.Glob(search_path):
-      word = re.search('.*/([^/]+)/.*.wav', wav_path).group(1).lower()
+      _, word = os.path.split(os.path.dirname(wav_path))
+      word = word.lower()
       # Treat the '_background_noise_' folder as a special case, since we expect
       # it to contain long audio samples we mix in to improve training.
       if word == BACKGROUND_NOISE_DIR_NAME:
diff --git a/tensorflow/examples/speech_commands/train.py b/tensorflow/examples/speech_commands/train.py
index a54bcbdb32..f5bf04305a 100644
--- a/tensorflow/examples/speech_commands/train.py
+++ b/tensorflow/examples/speech_commands/train.py
@@ -156,7 +156,8 @@ def main(_):
   predicted_indices = tf.argmax(logits, 1)
   expected_indices = tf.argmax(ground_truth_input, 1)
   correct_prediction = tf.equal(predicted_indices, expected_indices)
-  confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
+  confusion_matrix = tf.confusion_matrix(
+      expected_indices, predicted_indices, num_classes=label_count)
   evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   tf.summary.scalar('accuracy', evaluation_step)
 
diff --git a/tensorflow/examples/udacity/1_notmnist.ipynb b/tensorflow/examples/udacity/1_notmnist.ipynb
index 39674e1aa4..dffe5d37c6 100644
--- a/tensorflow/examples/udacity/1_notmnist.ipynb
+++ b/tensorflow/examples/udacity/1_notmnist.ipynb
@@ -46,13 +46,13 @@
         "# These are all the modules we'll be using later. Make sure you can import them\n",
         "# before proceeding further.\n",
         "from __future__ import print_function\n",
+        "import imageio\n",
         "import matplotlib.pyplot as plt\n",
         "import numpy as np\n",
         "import os\n",
         "import sys\n",
         "import tarfile\n",
         "from IPython.display import display, Image\n",
-        "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\n",
@@ -325,13 +325,13 @@
         "  for image in image_files:\n",
         "    image_file = os.path.join(folder, image)\n",
         "    try:\n",
-        "      image_data = (ndimage.imread(image_file).astype(float) - \n",
+        "      image_data = (imageio.imread(image_file).astype(float) - \n",
         "                    pixel_depth / 2) / pixel_depth\n",
         "      if image_data.shape != (image_size, image_size):\n",
         "        raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
         "      dataset[num_images, :, :] = image_data\n",
         "      num_images = num_images + 1\n",
-        "    except IOError as e:\n",
+        "    except (IOError, ValueError) as e:\n",
         "      print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
         "    \n",
         "  dataset = dataset[0:num_images, :, :]\n",