"""Trains the MNIST network using preloaded data in a constant.

Command to run this py_binary target:

bazel run -c opt \
    <...>/tensorflow/g3doc/how_tos/reading_data:fully_connected_preloaded
"""
import os.path
import time

import tensorflow.python.platform
import numpy
import tensorflow as tf

from tensorflow.g3doc.tutorials.mnist import input_data
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.  '
                     'Must divide evenly into the dataset sizes.')
flags.DEFINE_string('train_dir', 'data', 'Directory to put the training data.')
flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '
                     'for unit testing.')


def run_training():
    """Train MNIST for a number of epochs."""
    # Get the sets of images and labels for training, validation, and
    # test on MNIST.
    data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data)

    # Tell TensorFlow that the model will be built into the default Graph.
    with tf.Graph().as_default():
        with tf.name_scope('input'):
            # Input data
            input_images = tf.constant(data_sets.train.images)
            input_labels = tf.constant(data_sets.train.labels)

            image, label = tf.train.slice_input_producer(
                [input_images, input_labels], num_epochs=FLAGS.num_epochs)
            label = tf.cast(label, tf.int32)
            images, labels = tf.train.batch(
                [image, label], batch_size=FLAGS.batch_size)

        # Build a Graph that computes predictions from the inference model.
        logits = mnist.inference(images, FLAGS.hidden1, FLAGS.hidden2)

        # Add to the Graph the Ops for loss calculation.
        loss = mnist.loss(logits, labels)

        # Add to the Graph the Ops that calculate and apply gradients.
        train_op = mnist.training(loss, FLAGS.learning_rate)

        # Add the Op to compare the logits to the labels during evaluation.
        eval_correct = mnist.evaluation(logits, labels)

        # Build the summary operation based on the TF collection of Summaries.
        summary_op = tf.merge_all_summaries()

        # Create a saver for writing training checkpoints.
        saver = tf.train.Saver()

        # Create the op for initializing variables.
        init_op = tf.initialize_all_variables()

        # Create a session for running Ops on the Graph.
        sess = tf.Session()

        # Run the Op to initialize the variables.
        sess.run(init_op)

        # Instantiate a SummaryWriter to output summaries and the Graph.
        summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
                                                graph_def=sess.graph_def)

        # Start input enqueue threads.
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        # And then after everything is built, start the training loop.
        try:
            step = 0
            while not coord.should_stop():
                start_time = time.time()

                # Run one step of the model.
                _, loss_value = sess.run([train_op, loss])

                duration = time.time() - start_time

                # Write the summaries and print an overview fairly often.
                if step % 100 == 0:
                    # Print status to stdout.
                    print 'Step %d: loss = %.2f (%.3f sec)' % (step,
                                                               loss_value,
                                                               duration)
                    # Update the events file.
                    summary_str = sess.run(summary_op)
                    summary_writer.add_summary(summary_str, step)
                    step += 1

                # Save a checkpoint periodically.
                if (step + 1) % 1000 == 0:
                    print 'Saving'
                    saver.save(sess, FLAGS.train_dir, global_step=step)

                step += 1
        except tf.errors.OutOfRangeError:
            print 'Saving'
            saver.save(sess, FLAGS.train_dir, global_step=step)
            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()