Automated g4 rollback of changelist 179258973

PiperOrigin-RevId: 179260538

1 files changed, 68 insertions, 57 deletions

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 9db8835d92..a9ed02dd1a 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -45,7 +45,9 @@ TRAIN_FILE = 'train.tfrecords'
 VALIDATION_FILE = 'validation.tfrecords'
 
 
-def decode(serialized_example):
+def read_and_decode(filename_queue):
+  reader = tf.TFRecordReader()
+  _, serialized_example = reader.read(filename_queue)
   features = tf.parse_single_example(
       serialized_example,
       # Defaults are not specified since both keys are required.
@@ -58,26 +60,22 @@ def decode(serialized_example):
   # 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.
 
@@ -93,32 +91,31 @@ 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).
-
-    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.
+    Note that an tf.train.QueueRunner is added to the graph, which
+    must be run using e.g. tf.train.start_queue_runners().
   """
   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'):
-    # 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)
+    filename_queue = tf.train.string_input_producer(
+        [filename], num_epochs=num_epochs)
 
-    # map takes a python function and applies it to every sample
-    dataset = dataset.map(decode)
-    dataset = dataset.map(augment)
-    dataset = dataset.map(normalize)
+    # Even when reading in multiple threads, share the filename
+    # queue.
+    image, label = read_and_decode(filename_queue)
 
-    #the parameter is the queue size
-    dataset = dataset.shuffle(1000 + 3 * batch_size)
-    dataset = dataset.batch(batch_size)
+    # 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)
 
-    iterator = dataset.make_one_shot_iterator()
-  return iterator.get_next()
+    return images, sparse_labels
 
 
 def run_training():
@@ -127,16 +124,16 @@ def run_training():
   # Tell TensorFlow that the model will be built into the default Graph.
   with tf.Graph().as_default():
     # Input images and labels.
-    image_batch, label_batch = inputs(train=True, batch_size=FLAGS.batch_size,
-                               num_epochs=FLAGS.num_epochs)
+    images, labels = 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(image_batch,
+    logits = mnist.inference(images,
                              FLAGS.hidden1,
                              FLAGS.hidden2)
 
     # Add to the Graph the loss calculation.
-    loss = mnist.loss(logits, label_batch)
+    loss = mnist.loss(logits, labels)
 
     # Add to the Graph operations that train the model.
     train_op = mnist.training(loss, FLAGS.learning_rate)
@@ -146,33 +143,47 @@ def run_training():
                        tf.local_variables_initializer())
 
     # Create a session for running operations in the Graph.
-    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,
+    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,
                                                      duration))
-          step += 1
-      except tf.errors.OutOfRangeError:
-        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
-      
+        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()
+
+
 def main(_):
   run_training()