update how_tos/reading_data to use Dataset API

tensorflow/docs_src/api_guides/python/reading_data.md

@@ -173,14 +173,25 @@ For example,
 [`tensorflow/examples/how_tos/reading_data/convert_to_records.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/convert_to_records.py)
 converts MNIST data to this format.
 
-To read a file of TFRecords, use
-@{tf.TFRecordReader} with
-the @{tf.parse_single_example}
-decoder. The `parse_single_example` op decodes the example protocol buffers into
-tensors. An MNIST example using the data produced by `convert_to_records` can be
-found in
-[`tensorflow/examples/how_tos/reading_data/fully_connected_reader.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py),
-which you can compare with the `fully_connected_feed` version.
+The recommended way to read a TFRecord file is with a @{tf.data.TFRecordDataset}, [as in this example](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py):
+
+``` python
+    dataset = tf.data.TFRecordDataset(filename)
+    dataset = dataset.repeat(num_epochs)
+
+    # map takes a python function and applies it to every sample
+    dataset = dataset.map(decode)
+```
+
+To acomplish the same task with a queue based input pipeline requires the following code 
+(using the same `decode` function from the above example): 
+
+``` python
+  filename_queue = tf.train.string_input_producer([filename], num_epochs=num_epochs)
+  reader = tf.TFRecordReader()
+  _, serialized_example = reader.read(filename_queue)
+  image,label = decode(serialized_example)
+```
 
 ### Preprocessing
 

tensorflow/examples/how_tos/reading_data/fully_connected_reader.py

@@ -45,9 +45,7 @@
 VALIDATION_FILE = 'validation.tfrecords'
 
 
-def read_and_decode(filename_queue):
-  reader = tf.TFRecordReader()
-  _, serialized_example = reader.read(filename_queue)
+def decode(serialized_example):
   features = tf.parse_single_example(
       serialized_example,
       # Defaults are not specified since both keys are required.
@@ -60,22 +58,26 @@ def read_and_decode(filename_queue):
   # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
   # [mnist.IMAGE_PIXELS].
   image = tf.decode_raw(features['image_raw'], tf.uint8)
-  image.set_shape([mnist.IMAGE_PIXELS])
+  image.set_shape((mnist.IMAGE_PIXELS))
 
+  # Convert label from a scalar uint8 tensor to an int32 scalar.
+  label = tf.cast(features['label'], tf.int32)
+
+  return image, label
+
+def augment(image, label):
   # OPTIONAL: Could reshape into a 28x28 image and apply distortions
   # here.  Since we are not applying any distortions in this
   # example, and the next step expects the image to be flattened
   # into a vector, we don't bother.
+  return image, label
 
+def normalize(image, label):
   # Convert from [0, 255] -> [-0.5, 0.5] floats.
   image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
 
-  # Convert label from a scalar uint8 tensor to an int32 scalar.
-  label = tf.cast(features['label'], tf.int32)
-
   return image, label
 
-
 def inputs(train, batch_size, num_epochs):
   """Reads input data num_epochs times.
 
@@ -91,31 +93,32 @@ def inputs(train, batch_size, num_epochs):
       in the range [-0.5, 0.5].
     * labels is an int32 tensor with shape [batch_size] with the true label,
       a number in the range [0, mnist.NUM_CLASSES).
-    Note that an tf.train.QueueRunner is added to the graph, which
-    must be run using e.g. tf.train.start_queue_runners().
+
+    This function creates a one_shot_iterator, meaning that it will only iterate
+    over the dataset once. On the other hand there is no special initialization
+    required.
   """
   if not num_epochs: num_epochs = None
   filename = os.path.join(FLAGS.train_dir,
                           TRAIN_FILE if train else VALIDATION_FILE)
 
   with tf.name_scope('input'):
-    filename_queue = tf.train.string_input_producer(
-        [filename], num_epochs=num_epochs)
+    # TFRecordDataset opens a protobuf and reads entries line by line
+    # could also be [list, of, filenames]
+    dataset = tf.data.TFRecordDataset(filename)
+    dataset = dataset.repeat(num_epochs)
 
-    # Even when reading in multiple threads, share the filename
-    # queue.
-    image, label = read_and_decode(filename_queue)
+    # map takes a python function and applies it to every sample
+    dataset = dataset.map(decode)
+    dataset = dataset.map(augment)
+    dataset = dataset.map(normalize)
 
-    # Shuffle the examples and collect them into batch_size batches.
-    # (Internally uses a RandomShuffleQueue.)
-    # We run this in two threads to avoid being a bottleneck.
-    images, sparse_labels = tf.train.shuffle_batch(
-        [image, label], batch_size=batch_size, num_threads=2,
-        capacity=1000 + 3 * batch_size,
-        # Ensures a minimum amount of shuffling of examples.
-        min_after_dequeue=1000)
+    #the parameter is the queue size
+    dataset = dataset.shuffle(1000 + 3 * batch_size)
+    dataset = dataset.batch(batch_size)
 
-    return images, sparse_labels
+    iterator = dataset.make_one_shot_iterator()
+  return iterator.get_next()
 
 
 def run_training():
@@ -124,16 +127,16 @@ def run_training():
   # Tell TensorFlow that the model will be built into the default Graph.
   with tf.Graph().as_default():
     # Input images and labels.
-    images, labels = inputs(train=True, batch_size=FLAGS.batch_size,
-                            num_epochs=FLAGS.num_epochs)
+    image_batch, label_batch = inputs(train=True, batch_size=FLAGS.batch_size,
+                               num_epochs=FLAGS.num_epochs)
 
     # Build a Graph that computes predictions from the inference model.
-    logits = mnist.inference(images,
+    logits = mnist.inference(image_batch,
                              FLAGS.hidden1,
                              FLAGS.hidden2)
 
     # Add to the Graph the loss calculation.
-    loss = mnist.loss(logits, labels)
+    loss = mnist.loss(logits, label_batch)
 
     # Add to the Graph operations that train the model.
     train_op = mnist.training(loss, FLAGS.learning_rate)
@@ -143,47 +146,33 @@ def run_training():
                        tf.local_variables_initializer())
 
     # Create a session for running operations in the Graph.
-    sess = tf.Session()
-
-    # Initialize the variables (the trained variables and the
-    # epoch counter).
-    sess.run(init_op)
-
-    # Start input enqueue threads.
-    coord = tf.train.Coordinator()
-    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
-
-    try:
-      step = 0
-      while not coord.should_stop():
-        start_time = time.time()
-
-        # Run one step of the model.  The return values are
-        # the activations from the `train_op` (which is
-        # discarded) and the `loss` op.  To inspect the values
-        # of your ops or variables, you may include them in
-        # the list passed to sess.run() and the value tensors
-        # will be returned in the tuple from the call.
-        _, loss_value = sess.run([train_op, loss])
-
-        duration = time.time() - start_time
-
-        # Print an overview fairly often.
-        if step % 100 == 0:
-          print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
+    with tf.Session() as sess:
+      # Initialize the variables (the trained variables and the
+      # epoch counter).
+      sess.run(init_op)
+      try:
+        step = 0
+        while True: #train until OutOfRangeError
+          start_time = time.time()
+
+          # Run one step of the model.  The return values are
+          # the activations from the `train_op` (which is
+          # discarded) and the `loss` op.  To inspect the values
+          # of your ops or variables, you may include them in
+          # the list passed to sess.run() and the value tensors
+          # will be returned in the tuple from the call.
+          _, loss_value = sess.run([train_op, loss])
+
+          duration = time.time() - start_time
+
+          # Print an overview fairly often.
+          if step % 100 == 0:
+            print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
                                                      duration))
-        step += 1
-    except tf.errors.OutOfRangeError:
-      print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
-    finally:
-      # When done, ask the threads to stop.
-      coord.request_stop()
-
-    # Wait for threads to finish.
-    coord.join(threads)
-    sess.close()
-
-
+          step += 1
+      except tf.errors.OutOfRangeError:
+        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
+
 def main(_):
   run_training()