From e09808e0e4d05ffe4be97f563f0f14ae2ebb20dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 28 Jun 2016 23:51:09 -0800
Subject: Fixing broken code in how_tos for fully_connected_reader.

 - Added initialize_local_variables()
 - Updated the code that generates the dataset since it no longer worked either.
 - This in turn required adding a "reshape" option that controls whether or not the imaged data is flattened.
Change: 126165145
---
 .../how_tos/reading_data/convert_to_records.py     | 42 ++++++++--------------
 .../how_tos/reading_data/fully_connected_reader.py |  3 +-
 2 files changed, 17 insertions(+), 28 deletions(-)

(limited to 'tensorflow/examples/how_tos')

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 ee558f5b19..2e3035731a 100644
--- a/tensorflow/examples/how_tos/reading_data/convert_to_records.py
+++ b/tensorflow/examples/how_tos/reading_data/convert_to_records.py
@@ -21,9 +21,11 @@ from __future__ import print_function
 import os
 import numpy
 import tensorflow as tf
-from tensorflow.examples.tutorials.mnist import input_data
+from tensorflow.contrib.learn.python.learn.datasets import mnist
 
 
+SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
+
 TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'  # MNIST filenames
 TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
 TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
@@ -47,10 +49,13 @@ def _bytes_feature(value):
   return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
 
 
-def convert_to(images, labels, name):
-  num_examples = labels.shape[0]
+def convert_to(data_set, name):
+  images = data_set.images
+  labels = data_set.labels
+  num_examples = data_set.num_examples
+
   if images.shape[0] != num_examples:
-    raise ValueError("Images size %d does not match label size %d." %
+    raise ValueError('Images size %d does not match label size %d.' %
                      (images.shape[0], num_examples))
   rows = images.shape[1]
   cols = images.shape[2]
@@ -73,31 +78,14 @@ def convert_to(images, labels, name):
 
 def main(argv):
   # Get the data.
-  train_images_filename = input_data.maybe_download(
-      TRAIN_IMAGES, FLAGS.directory)
-  train_labels_filename = input_data.maybe_download(
-      TRAIN_LABELS, FLAGS.directory)
-  test_images_filename = input_data.maybe_download(
-      TEST_IMAGES, FLAGS.directory)
-  test_labels_filename = input_data.maybe_download(
-      TEST_LABELS, FLAGS.directory)
-
-  # Extract it into numpy arrays.
-  train_images = input_data.extract_images(train_images_filename)
-  train_labels = input_data.extract_labels(train_labels_filename)
-  test_images = input_data.extract_images(test_images_filename)
-  test_labels = input_data.extract_labels(test_labels_filename)
-
-  # Generate a validation set.
-  validation_images = train_images[:FLAGS.validation_size, :, :, :]
-  validation_labels = train_labels[:FLAGS.validation_size]
-  train_images = train_images[FLAGS.validation_size:, :, :, :]
-  train_labels = train_labels[FLAGS.validation_size:]
+  data_sets = mnist.read_data_sets(FLAGS.directory,
+                                   dtype=tf.uint8,
+                                   reshape=False)
 
   # Convert to Examples and write the result to TFRecords.
-  convert_to(train_images, train_labels, 'train')
-  convert_to(validation_images, validation_labels, 'validation')
-  convert_to(test_images, test_labels, 'test')
+  convert_to(data_sets.train, 'train')
+  convert_to(data_sets.validation, 'validation')
+  convert_to(data_sets.test, 'test')
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
index 648825e6e9..bdd821373f 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -146,7 +146,8 @@ def run_training():
     train_op = mnist.training(loss, FLAGS.learning_rate)
 
     # The op for initializing the variables.
-    init_op = tf.initialize_all_variables()
+    init_op = tf.group(tf.initialize_all_variables(),
+                       tf.initialize_local_variables())
 
     # Create a session for running operations in the Graph.
     sess = tf.Session()
-- 
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