Return datasets directly from input_fns for TF1.5

official/README.md

@@ -2,11 +2,11 @@
 
 The TensorFlow official models are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.
 
-The master branch of the models are **in development**, and they target the [nightly binaries](https://github.com/tensorflow/tensorflow#installation) built from the [master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master).
+The master branch of the models are **in development**, and they target the [nightly binaries](https://github.com/tensorflow/tensorflow#installation) built from the [master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master). We aim to keep them backwards compatible with the latest release when possible (currently TensorFlow 1.5), but we cannot always guarantee compatibility.
 
 **Stable versions** of the official models targeting releases of TensorFlow are available as tagged branches or [downloadable releases](https://github.com/tensorflow/models/releases). Model repository version numbers match the target TensorFlow release, such that [branch r1.4.0](https://github.com/tensorflow/models/tree/r1.4.0) and [release v1.4.0](https://github.com/tensorflow/models/releases/tag/v1.4.0) are compatible with [TensorFlow v1.4.0](https://github.com/tensorflow/tensorflow/releases/tag/v1.4.0).
 
-If you are on a version of TensorFlow earlier than v1.4, please [update your installation](https://www.tensorflow.org/install/).
+If you are on a version of TensorFlow earlier than 1.4, please [update your installation](https://www.tensorflow.org/install/).
 
 
 ---

official/mnist/mnist.py

@@ -184,8 +184,7 @@ def main(unused_argv):
     ds = dataset.train(FLAGS.data_dir)
     ds = ds.cache().shuffle(buffer_size=50000).batch(FLAGS.batch_size).repeat(
         FLAGS.train_epochs)
-    (images, labels) = ds.make_one_shot_iterator().get_next()
-    return (images, labels)
+    return ds
 
   # Set up training hook that logs the training accuracy every 100 steps.
   tensors_to_log = {'train_accuracy': 'train_accuracy'}

official/wide_deep/wide_deep.py

@@ -192,10 +192,7 @@ def input_fn(data_file, num_epochs, shuffle, batch_size):
   # epochs from blending together.
   dataset = dataset.repeat(num_epochs)
   dataset = dataset.batch(batch_size)
-
-  iterator = dataset.make_one_shot_iterator()
-  features, labels = iterator.get_next()
-  return features, labels
+  return dataset
 
 
 def main(unused_argv):