b06553463a869bcc7d8fd502c5ba284c96148fd4,main.py,,run_training,#,37

Before Change

    // 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)
    for i in (1, 2, 3, 4):
        start_time = time.time()
        // Tell TensorFlow that the model will be built into the default Graph.
        with tf.Graph().as_default():
            graph = MNISTGraph(
                learning_rate=FLAGS.learning_rate, hidden1=FLAGS.hidden1//2*i,

After Change

    Train MNIST for a number of steps.
    // 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)
    best_geometry = brute_force_optimal_network_geometry(data_sets, FLAGS.training_precision)
    print(best_geometry)
    start_time = time.time()
    with tf.Graph().as_default():
        graph = MNISTGraph(
            learning_rate=FLAGS.learning_rate,
            hidden1=best_geometry[3][0], hidden2=best_geometry[3][1], hidden3=best_geometry[3][2],
            batch_size=FLAGS.batch_size, train_dir=FLAGS.train_dir
        )
        graph.train(data_sets, FLAGS.max_steps, precision=FLAGS.desired_precision)

Instances