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diff --git a/tensorflow/g3doc/how_tos/reading_data/fully_connected_reader.py b/tensorflow/g3doc/how_tos/reading_data/fully_connected_reader.py
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+"""Train and Eval the MNIST network.
+
+This version is like fully_connected_feed.py but uses data converted
+to a TFRecords file containing tf.train.Example protocol buffers.
+See tensorflow/g3doc/how_tos/reading_data.md#reading-from-files
+for context.
+
+YOU MUST run convert_to_records before running this (but you only need to
+run it once).
+"""
+
+import os.path
+import time
+
+import tensorflow.python.platform
+import numpy
+import tensorflow as tf
+
+from tensorflow.g3doc.tutorials.mnist import mnist
+
+
+# Basic model parameters as external flags.
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
+flags.DEFINE_integer('num_epochs', 2, 'Number of epochs to run trainer.')
+flags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.')
+flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')
+flags.DEFINE_integer('batch_size', 100, 'Batch size.')
+flags.DEFINE_string('train_dir', 'data', 'Directory with the training data.')
+
+# Constants used for dealing with the files, matches convert_to_records.
+TRAIN_FILE = 'train.tfrecords'
+VALIDATION_FILE = 'validation.tfrecords'
+
+
+def read_and_decode(filename_queue):
+    reader = tf.TFRecordReader()
+    _, serialized_example = reader.read(filename_queue)
+    features = tf.parse_single_example(
+        serialized_example,
+        dense_keys=['image_raw', 'label'],
+        # Defaults are not specified since both keys are required.
+        dense_types=[tf.string, tf.int64])
+
+    # Convert from a scalar string tensor (whose single string has
+    # 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])
+
+    # 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.
+
+    # 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.
+
+    Args:
+      train: Selects between the training (True) and validation (False) data.
+      batch_size: Number of examples per returned batch.
+      num_epochs: Number of times to read the input data, or 0/None to
+         train forever.
+
+    Returns:
+      A tuple (images, labels), where:
+      * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
+        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().
+    """
+    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)
+
+        # Even when reading in multiple threads, share the filename
+        # queue.
+        image, label = read_and_decode(filename_queue)
+
+        # 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)
+
+        return images, sparse_labels
+
+
+def run_training():
+    """Train MNIST for a number of steps."""
+
+    # 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)
+
+        # Build a Graph that computes predictions from the inference model.
+        logits = mnist.inference(images,
+                                 FLAGS.hidden1,
+                                 FLAGS.hidden2)
+
+        # Add to the Graph the loss calculation.
+        loss = mnist.loss(logits, labels)
+
+        # Add to the Graph operations that train the model.
+        train_op = mnist.training(loss, FLAGS.learning_rate)
+
+        # The op for initializing the variables.
+        init_op = tf.initialize_all_variables();
+
+        # 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,
+                                                               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()
+
+
+def main(_):
+    run_training()
+
+
+if __name__ == '__main__':
+    tf.app.run()