Function run_training

tensorflow/contrib/factorization/examples/mnist.py:183–276

Train MNIST for a number of steps.

()

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

  # Tell TensorFlow that the model will be built into the default Graph.
  with tf.Graph().as_default():
    # Generate placeholders for the images and labels.
    images_placeholder, labels_placeholder = placeholder_inputs()

    # Build a Graph that computes predictions from the inference model.
    logits, clustering_loss, kmeans_init, kmeans_training_op = inference(
        images_placeholder,
        FLAGS.num_clusters,
        FLAGS.hidden1,
        FLAGS.hidden2)

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

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

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

    # Add the variable initializer Op.
    init = tf.global_variables_initializer()

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

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

    feed_dict = fill_feed_dict(data_sets.train,
                               images_placeholder,
                               labels_placeholder,
                               batch_size=max(FLAGS.batch_size, 5000))
    # Run the Op to initialize the clusters.
    sess.run(kmeans_init, feed_dict=feed_dict)

    # Start the training loop.
    max_test_prec = 0
    for step in xrange(FLAGS.max_steps):
      start_time = time.time()

      # Fill a feed dictionary with the actual set of images and labels
      # for this particular training step.
      feed_dict = fill_feed_dict(data_sets.train,
                                 images_placeholder,
                                 labels_placeholder,
                                 FLAGS.batch_size)

      # Run one step of the model.